Distributed processing of pose graphs for generating high definition maps for navigating autonomous vehicles

ABSTRACT

A high-definition map system receives sensor data from vehicles travelling along routes and combines the data to generate a high definition map for use in driving vehicles, for example, for guiding autonomous vehicles. A pose graph is built from the collected data, each pose representing location and orientation of a vehicle. The pose graph is optimized to minimize constraints between poses. Points associated with surface are assigned a confidence measure determined using a measure of hardness/softness of the surface. A machine-learning-based result filter detects bad alignment results and prevents them from being entered in the subsequent global pose optimization. The alignment framework is parallelizable for execution using a parallel/distributed architecture. Alignment hot spots are detected for further verification and improvement. The system supports incremental updates, thereby allowing refinements of subgraphs for incrementally improving the high-definition map for keeping it up to date

CROSS-REFERENCE TO RELATED APPLICATION

This application claims the benefit of U.S. Provisional Application No.62/441,080, filed Dec. 30, 2016, which is hereby incorporated byreference in its entirety.

BACKGROUND

This disclosure relates generally to maps for autonomous vehicles, andmore particularly to performing alignment of three dimensionalrepresentation of data captured by autonomous vehicles to generate highdefinition maps for safe navigation of autonomous vehicles.

Autonomous vehicles, also known as self-driving cars, driverless cars,auto, or robotic cars, drive from a source location to a destinationlocation without requiring a human driver to control and navigate thevehicle. Automation of driving is difficult due to several reasons. Forexample, autonomous vehicles use sensors to make driving decisions onthe fly, but vehicle sensors cannot observe everything all the time.Vehicle sensors can be obscured by corners, rolling hills, and othervehicles. Vehicles sensors may not observe certain things early enoughto make decisions. In addition, lanes and signs may be missing on theroad or knocked over or hidden by bushes, and therefore not detectableby sensors. Furthermore, road signs for rights of way may not be readilyvisible for determining from where vehicles could be coming, or forswerving or moving out of a lane in an emergency or when there is astopped obstacle that must be passed.

Autonomous vehicles can use map data to figure out some of the aboveinformation instead of relying on sensor data. However conventional mapshave several drawbacks that make them difficult to use for an autonomousvehicle. For example maps do not provide the level of accuracy requiredfor safe navigation (e.g., 10 cm or less). GNSS (Global NavigationSatellite System) based systems provide accuracies of approximately 3-5meters, but have large error conditions resulting in an accuracy of over100 m. This makes it challenging to accurately determine the location ofthe vehicle.

Furthermore, conventional maps are created by survey teams that usedrivers with specially outfitted cars with high resolution sensors thatdrive around a geographic region and take measurements. The measurementsare taken back and a team of map editors assembles the map from themeasurements. This process is expensive and time consuming (e.g., takingpossibly months to complete a map). Therefore, maps assembled using suchtechniques do not have fresh data. For example, roads areupdated/modified on a frequent basis roughly 5-10% per year. But surveycars are expensive and limited in number, so cannot capture most ofthese updates. For example, a survey fleet may include a thousand cars.For even a single state in the United States, a thousand cars would notbe able to keep the map up-to-date on a regular basis to allow safeself-driving. As a result, conventional techniques of maintaining mapsare unable to provide the right data that is sufficiently accurate andup-to-date for safe navigation of autonomous vehicles.

SUMMARY

Embodiments receive sensor data from vehicles traveling along routeswithin a geographical region and combine the data to generate a highdefinition map. The high definition map is for use in driving vehicles,for example, for guiding autonomous vehicles.

A system receives sensor data captured by vehicles driving through apath in a geographical region. Examples of sensor data include datacollected by LIDAR, data collected by global positioning system (GPS),or data collected by inertial measurement unit (IMU). The systemgenerates a pose graph based on the sensor data. Each node of the graphrepresents a pose of a vehicle, describing a location and orientation ofthe vehicle. An edge between a pair of nodes represents a transformationbetween nodes of the pair of nodes. The system divides the pose graphinto a plurality of subgraphs. Each subgraph includes a set of corenodes and a set of buffer nodes. The system iteratively performs thesteps for optimizing sub pose graphs while keeping values of boundarynodes fixed and updating only the core node poses of the sub posegraphs. The system generates a high-definition map based on the pointcloud representation and sends the high-definition map to autonomousvehicles for use in navigation of the autonomous vehicles.

In an embodiment, the system distributes the subgraphs among a pluralityof processors for distributed execution. The processors exchangeinformation to determine boundary node values at the end of eachiteration.

In an embodiment, the system determines whether there are changes inboundary nodes as a result of updating all node poses. The system stopsthe iterations responsive to determining that the changes to boundarynodes are below a threshold value, for example, if there are no changesin the boundary nodes during an iteration

BRIEF DESCRIPTION OF THE DRAWINGS

FIG. 1 shows the overall system environment of an HD map systeminteracting with multiple vehicle computing systems, according to anembodiment.

FIG. 2 shows the system architecture of a vehicle computing system,according to an embodiment.

FIG. 3 illustrates the various layers of instructions in the HD Map APIof a vehicle computing system, according to an embodiment.

FIG. 4 shows the system architecture of an HD map system, according toan embodiment.

FIG. 5 illustrates the components of an HD map, according to anembodiment.

FIGS. 6A-B illustrate geographical regions defined in an HD map,according to an embodiment.

FIG. 7 illustrates representations of lanes in an HD map, according toan embodiment.

FIGS. 8A-B illustrates lane elements and relations between lane elementsin an HD map, according to an embodiment.

FIGS. 9A-B illustrate coordinate systems for use by the HD map system,according to an embodiment.

FIG. 10 illustrates the process of LIDAR point cloud unwinding by the HDmap system, according to an embodiment.

FIG. 11 shows the system architecture of the global alignment module,according to an embodiment.

FIG. 12(A) illustrates single track pairwise alignment process accordingto an embodiment.

FIG. 12(B) illustrates global alignment process according to anembodiment.

FIG. 13 shows a visualization of a pose graph according to anembodiment.

FIG. 14 shows a visualization of a pose graph that includes pose priorsbased on GNSS, according to an embodiment.

FIG. 15 shows a flowchart illustrating the process for performing posegraph optimization, according to an embodiment.

FIG. 16 shows the system architecture of a machine learning based ICPResult filter, according to an embodiment.

FIG. 17 shows a process for training a model for machine learning basedICP result filter, according to an embodiment.

FIG. 18 shows a process for performing ICP result filter using a machinelearning based model, according to an embodiment.

FIG. 19(A) shows an example subgraph from a pose graph, according to anembodiment.

FIG. 19(B) illustrate division of a pose graph into subgraphs fordistributed execution of the pose graph optimization, according to anembodiment.

FIG. 20 shows a process for distributed processing of a pose graph,according to an embodiment.

FIG. 21 shows a process for distributed optimization of a pose graph,according to an embodiment.

FIG. 22 shows an example illustrating the process of incremental updatesto a pose graph, according to an embodiment.

FIG. 23 shows a flowchart illustrating the process of incrementalupdates to a pose graph, according to an embodiment.

FIG. 24 illustrates the ICP process performed by the HD map systemaccording to an embodiment.

FIG. 25 illustrates estimation of point cloud normals using the LiDARrange image by the HD map system, according to an embodiment.

FIG. 26 shows the process for determining point cloud normals using theLiDAR range image by the HD map system, according to an embodiment.

FIG. 27 shows the process for performing pairwise alignment based onclassification of surfaces as hard/soft, according to an embodiment.

FIG. 28 shows the process for determining a measure of confidence forpoints along a surface for use in pairwise alignment, according to anembodiment.

FIG. 29 shows the process for determining a measure of confidence forpoints, according to an embodiment.

FIG. 30 shows a process for automatic detection of misalignment hotspot,according to an embodiment.

FIG. 31 shows a process for detection of misalignment for surfacesrepresented in a point cloud, according to an embodiment.

FIG. 32 illustrates detection of misalignment for ground represented ina point cloud, according to an embodiment.

FIG. 33 shows a process for detection of misalignment for ground surfacerepresented in a point cloud, according to an embodiment.

FIG. 34 shows a process for detection of misalignment for verticalsurfaces represented in a point cloud, according to an embodiment.

FIG. 35 shows an example illustrating detection of misalignment for avertical structure such as a wall represented in a point cloud,according to an embodiment.

FIG. 36 illustrates an embodiment of a computing machine that can readinstructions from a machine-readable medium and execute the instructionsin a processor or controller.

The figures depict various embodiments of the present invention forpurposes of illustration only. One skilled in the art will readilyrecognize from the following discussion that alternative embodiments ofthe structures and methods illustrated herein may be employed withoutdeparting from the principles of the invention described herein.

DETAILED DESCRIPTION Overview

Embodiments of the invention maintain high definition (HD) mapscontaining up to date information using high precision. The HD maps maybe used by autonomous vehicles to safely navigate to their destinationswithout human input or with limited human input. An autonomous vehicleis a vehicle capable of sensing its environment and navigating withouthuman input. Autonomous vehicles may also be referred to herein as“driverless car,” “self-driving car,” or “robotic car.” An HD map refersto a map storing data with very high precision, typically 5-10 cm.Embodiments generate HD maps containing spatial geometric informationabout the roads on which an autonomous vehicle can travel. Accordingly,the generated HD maps include the information necessary for anautonomous vehicle navigating safely without human intervention. Insteadof collecting data for the HD maps using an expensive and time consumingmapping fleet process including vehicles outfitted with high resolutionsensors, embodiments of the invention use data from the lower resolutionsensors of the self-driving vehicles themselves as they drive aroundthrough their environments. The vehicles may have no prior map data forthese routes or even for the region. Embodiments of the inventionprovide location as a service (LaaS) such that autonomous vehicles ofdifferent manufacturers can each have access to the most up-to-date mapinformation created via these embodiments of invention.

Embodiments generate and maintain high definition (HD) maps that areaccurate and include the most updated road conditions for safenavigation. For example, the HD maps provide the current location of theautonomous vehicle relative to the lanes of the road precisely enough toallow the autonomous vehicle to drive safely in the lane.

HD maps store a very large amount of information, and therefore facechallenges in managing the information. For example, an HD map for alarge geographic region may not fit on the local storage of a vehicle.Embodiments of the invention provide the necessary portion of an HD mapto an autonomous vehicle that allows the vehicle to determine itscurrent location in the HD map, determine the features on the roadrelative to the vehicle's position, determine if it is safe to move thevehicle based on physical constraints and legal constraints, etc.Examples of physical constraints include physical obstacles, such aswalls, and examples of legal constraints include legally alloweddirection of travel for a lane, speed limits, yields, stops.

Embodiments of the invention allow safe navigation for an autonomousvehicle by providing low latency, for example, 10-20 milliseconds orless for providing a response to a request; high accuracy in terms oflocation, i.e., accuracy within 10 cm or less; freshness of data byensuring that the map is updated to reflect changes on the road within areasonable time frame; and storage efficiency by minimizing the storageneeded for the HD Map.

FIG. 1 shows the overall system environment of an HD map systeminteracting with multiple vehicles, according to an embodiment. The HDmap system 100 includes an online HD map system 110 that interacts witha plurality of vehicles 150. The vehicles 150 may be autonomous vehiclesbut are not required to be. The online HD map system 110 receives sensordata captured by sensors of the vehicles, and combines the data receivedfrom the vehicles 150 to generate and maintain HD maps. The online HDmap system 110 sends HD map data to the vehicles for use in driving thevehicles. In an embodiment, the online HD map system 110 is implementedas a distributed computing system, for example, a cloud based servicethat allows clients such as vehicle computing systems 120 to makerequests for information and services. For example, a vehicle computingsystem 120 may make a request for HD map data for driving along a routeand the online HD map system 110 provides the requested HD map data.

FIG. 1 and the other figures use like reference numerals to identifylike elements. A letter after a reference numeral, such as “105A,”indicates that the text refers specifically to the element having thatparticular reference numeral. A reference numeral in the text without afollowing letter, such as “105,” refers to any or all of the elements inthe figures bearing that reference numeral (e.g. “105” in the textrefers to reference numerals “105A” and/or “105N” in the figures).

The online HD map system 110 comprises a vehicle interface module 160and an HD map store 165. The online HD map system 110 interacts with thevehicle computing system 120 of various vehicles 150 using the vehicleinterface module 160. The online HD map system 110 stores mapinformation for various geographical regions in the HD map store 165.The online HD map system 110 may include other modules than those shownin FIG. 1, for example, various other modules as illustrated in FIG. 4and further described herein.

The online HD map system 110 receives 115 data collected by sensors of aplurality of vehicles 150, for example, hundreds or thousands of cars.The vehicles provide sensor data captured while driving along variousroutes and send it to the online HD map system 110. The online HD mapsystem 110 uses the data received from the vehicles 150 to create andupdate HD maps describing the regions in which the vehicles 150 aredriving. The online HD map system 110 builds high definition maps basedon the collective information received from the vehicles 150 and storesthe HD map information in the HD map store 165.

The online HD map system 110 sends 125 HD maps to individual vehicles150 as required by the vehicles 150. For example, if an autonomousvehicle needs to drive along a route, the vehicle computing system 120of the autonomous vehicle provides information describing the routebeing traveled to the online HD map system 110. In response, the onlineHD map system 110 provides the required HD maps for driving along theroute.

In an embodiment, the online HD map system 110 sends portions of the HDmap data to the vehicles in a compressed format so that the datatransmitted consumes less bandwidth. The online HD map system 110receives from various vehicles, information describing the data that isstored at the local HD map store 275 of the vehicle. If the online HDmap system 110 determines that the vehicle does not have certain portionof the HD map stored locally in the local HD map store 275, the onlineHD map system 110 sends that portion of the HD map to the vehicle. Ifthe online HD map system 110 determines that the vehicle did previouslyreceive that particular portion of the HD map but the corresponding datawas updated by the online HD map system 110 since the vehicle lastreceived the data, the online HD map system 110 sends an update for thatportion of the HD map stored at the vehicle. This allows the online HDmap system 110 to minimize the amount of data that is communicated withthe vehicle and also to keep the HD map data stored locally in thevehicle updated on a regular basis.

A vehicle 150 includes vehicle sensors 105, vehicle controls 130, and avehicle computing system 120. The vehicle sensors 105 allow the vehicle150 to detect the surroundings of the vehicle as well as informationdescribing the current state of the vehicle, for example, informationdescribing the location and motion parameters of the vehicle. Thevehicle sensors 105 comprise a camera, a light detection and rangingsensor (LIDAR), a GNSS navigation system, an inertial measurement unit(IMU), and others. The vehicle has one or more cameras that captureimages of the surroundings of the vehicle. A LIDAR surveys thesurroundings of the vehicle by measuring distance to a target byilluminating that target with a laser light pulses, and measuring thereflected pulses. The GNSS navigation system determines the position ofthe vehicle based on signals from satellites. An IMU is an electronicdevice that measures and reports motion data of the vehicle such asvelocity, acceleration, direction of movement, speed, angular rate, andso on using a combination of accelerometers and gyroscopes or othermeasuring instruments.

The vehicle controls 130 control the physical movement of the vehicle,for example, acceleration, direction change, starting, stopping, and soon. The vehicle controls 130 include the machinery for controlling theaccelerator, brakes, steering wheel, and so on. The vehicle computingsystem 120 continuously provides control signals to the vehicle controls130, thereby causing an autonomous vehicle to drive along a selectedroute.

The vehicle computing system 120 performs various tasks includingprocessing data collected by the sensors as well as map data receivedfrom the online HD map system 110. The vehicle computing system 120 alsoprocesses data for sending to the online HD map system 110. Details ofthe vehicle computing system are illustrated in FIG. 2 and furtherdescribed in connection with FIG. 2.

The interactions between the vehicle computing systems 120 and theonline HD map system 110 are typically performed via a network, forexample, via the Internet. The network enables communications betweenthe vehicle computing systems 120 and the online HD map system 110. Inone embodiment, the network uses standard communications technologiesand/or protocols. The data exchanged over the network can be representedusing technologies and/or formats including the hypertext markuplanguage (HTML), the extensible markup language (XML), etc. In addition,all or some of links can be encrypted using conventional encryptiontechnologies such as secure sockets layer (SSL), transport layersecurity (TLS), virtual private networks (VPNs), Internet Protocolsecurity (IPsec), etc. In another embodiment, the entities can usecustom and/or dedicated data communications technologies instead of, orin addition to, the ones described above.

FIG. 2 shows the system architecture of a vehicle computing system,according to an embodiment. The vehicle computing system 120 comprises aperception module 210, prediction module 215, planning module 220, acontrol module 225, a local HD map store 275, an HD map system interface280, and an HD map application programming interface (API) 205. Thevarious modules of the vehicle computing system 120 process various typeof data including sensor data 230, a behavior model 235, routes 240, andphysical constraints 245. In other embodiments, the vehicle computingsystem 120 may have more or fewer modules. Functionality described asbeing implemented by a particular module may be implemented by othermodules.

The perception module 210 receives sensor data 230 from the sensors 105of the vehicle 150. This includes data collected by cameras of the car,LIDAR, IMU, GNSS navigation system, and so on. The perception module 210uses the sensor data to determine what objects are around the vehicle,the details of the road on which the vehicle is travelling, and so on.The perception module 210 processes the sensor data 230 to populate datastructures storing the sensor data and provides the information to theprediction module 215.

The prediction module 215 interprets the data provided by the perceptionmodule using behavior models of the objects perceived to determinewhether an object is moving or likely to move. For example, theprediction module 215 may determine that objects representing road signsare not likely to move, whereas objects identified as vehicles, people,and so on, are either moving or likely to move. The prediction module215 uses the behavior models 235 of various types of objects todetermine whether they are likely to move. The prediction module 215provides the predictions of various objects to the planning module 200to plan the subsequent actions that the vehicle needs to take next.

The planning module 200 receives the information describing thesurroundings of the vehicle from the prediction module 215, the route240 that determines the destination of the vehicle, and the path thatthe vehicle should take to get to the destination. The planning module200 uses the information from the prediction module 215 and the route240 to plan a sequence of actions that the vehicle needs to take withina short time interval, for example, within the next few seconds. In anembodiment, the planning module 200 specifies the sequence of actions asone or more points representing nearby locations that the vehicle needsto drive through next. The planning module 200 provides the details ofthe plan comprising the sequence of actions to be taken by the vehicleto the control module 225. The plan may determine the subsequent actionof the vehicle, for example, whether the vehicle performs a lane change,a turn, acceleration by increasing the speed or slowing down, and so on.

The control module 225 determines the control signals for sending to thecontrols 130 of the vehicle based on the plan received from the planningmodule 200. For example, if the vehicle is currently at point A and theplan specifies that the vehicle should next go to a nearby point B, thecontrol module 225 determines the control signals for the controls 130that would cause the vehicle to go from point A to point B in a safe andsmooth way, for example, without taking any sharp turns or a zig zagpath from point A to point B. The path taken by the vehicle to go frompoint A to point B may depend on the current speed and direction of thevehicle as well as the location of point B with respect to point A. Forexample, if the current speed of the vehicle is high, the vehicle maytake a wider turn compared to a vehicle driving slowly.

The control module 225 also receives physical constraints 245 as input.These include the physical capabilities of that specific vehicle. Forexample, a car having a particular make and model may be able to safelymake certain types of vehicle movements such as acceleration, and turnsthat another car with a different make and model may not be able to makesafely. The control module 225 incorporates these physical constraintsin determining the control signals. The control module 225 sends thecontrol signals to the vehicle controls 130 that cause the vehicle toexecute the specified sequence of actions causing the vehicle to move asplanned. The above steps are constantly repeated every few secondscausing the vehicle to drive safely along the route that was planned forthe vehicle.

The various modules of the vehicle computing system 120 including theperception module 210, prediction module 215, and planning module 220receive map information to perform their respective computation. Thevehicle 100 stores the HD map data in the local HD map store 275. Themodules of the vehicle computing system 120 interact with the map datausing the HD map API 205 that provides a set of application programminginterfaces (APIs) that can be invoked by a module for accessing the mapinformation. The HD map system interface 280 allows the vehiclecomputing system 120 to interact with the online HD map system 110 via anetwork (not shown in the Figures). The local HD map store 275 storesmap data in a format specified by the HD Map system 110. The HD map API205 is capable of processing the map data format as provided by the HDMap system 110. The HD Map API 205 provides the vehicle computing system120 with an interface for interacting with the HD map data. The HD mapAPI 205 includes several APIs including the localization API 250, thelandmark map API 255, the route API 265, the 3D map API 270, the mapupdate API 285, and so on.

The localization APIs 250 determine the current location of the vehicle,for example, when the vehicle starts and as the vehicle moves along aroute. The localization APIs 250 include a localize API that determinesan accurate location of the vehicle within the HD Map. The vehiclecomputing system 120 can use the location as an accurate relativepositioning for making other queries, for example, feature queries,navigable space queries, and occupancy map queries further describedherein. The localize API receives inputs comprising one or more of,location provided by GNSS, vehicle motion data provided by IMU, LIDARscanner data, and camera images. The localize API returns an accuratelocation of the vehicle as latitude and longitude coordinates. Thecoordinates returned by the localize API are more accurate compared tothe GNSS coordinates used as input, for example, the output of thelocalize API may have precision range from 5-10 cm. In one embodiment,the vehicle computing system 120 invokes the localize API to determinelocation of the vehicle periodically based on the LIDAR using scannerdata, for example, at a frequency of 10 Hz. The vehicle computing system120 may invoke the localize API to determine the vehicle location at ahigher rate (e.g., 60 Hz) if GNSS/IMU data is available at that rate.The vehicle computing system 120 stores as internal state, locationhistory records to improve accuracy of subsequent localize calls. Thelocation history record stores history of location from thepoint-in-time, when the car was turned off/stopped. The localizationAPIs 250 include a localize-route API generates an accurate routespecifying lanes based on the HD map. The localize-route API takes asinput a route from a source to destination via a third party maps andgenerates a high precision routes represented as a connected graph ofnavigable lanes along the input routes based on HD maps.

The landmark map API 255 provides the geometric and semantic descriptionof the world around the vehicle, for example, description of variousportions of lanes that the vehicle is currently travelling on. Thelandmark map APIs 255 comprise APIs that allow queries based on landmarkmaps, for example, fetch-lanes API and fetch-features API. Thefetch-lanes API provide lane information relative to the vehicle and thefetch-features API. The fetch-lanes API receives as input a location,for example, the location of the vehicle specified using latitude andlongitude of the vehicle and returns lane information relative to theinput location. The fetch-lanes API may specify a distance parametersindicating the distance relative to the input location for which thelane information is retrieved. The fetch-features API receivesinformation identifying one or more lane elements and returns landmarkfeatures relative to the specified lane elements. The landmark featuresinclude, for each landmark, a spatial description that is specific tothe type of landmark.

The 3D map API 265 provides efficient access to the spatial3-dimensional (3D) representation of the road and various physicalobjects around the road as stored in the local HD map store 275. The 3Dmap APIs 365 include a fetch-navigable-surfaces API and afetch-occupancy-grid API. The fetch-navigable-surfaces API receives asinput, identifiers for one or more lane elements and returns navigableboundaries for the specified lane elements. The fetch-occupancy-grid APIreceives a location as input, for example, a latitude and longitude ofthe vehicle, and returns information describing occupancy for thesurface of the road and all objects available in the HD map near thelocation. The information describing occupancy includes a hierarchicalvolumetric grid of all positions considered occupied in the map. Theoccupancy grid includes information at a high resolution near thenavigable areas, for example, at curbs and bumps, and relatively lowresolution in less significant areas, for example, trees and wallsbeyond a curb. The fetch-occupancy-grid API is useful for detectingobstacles and for changing direction if necessary.

The 3D map APIs also include map update APIs, for example,download-map-updates API and upload-map-updates API. Thedownload-map-updates API receives as input a planned route identifierand downloads map updates for data relevant to all planned routes or fora specific planned route. The upload-map-updates API uploads datacollected by the vehicle computing system 120 to the online HD mapsystem 110. This allows the online HD map system 110 to keep the HD mapdata stored in the online HD map system 110 up to date based on changesin map data observed by sensors of vehicles driving along variousroutes.

The route API 270 returns route information including full route betweena source and destination and portions of route as the vehicle travelsalong the route. The 3D map API 365 allows querying the HD Map. Theroute APIs 270 include add-planned-routes API and get-planned-route API.The add-planned-routes API provides information describing plannedroutes to the online HD map system 110 so that information describingrelevant HD maps can be downloaded by the vehicle computing system 120and kept up to date. The add-planned-routes API receives as input, aroute specified using polylines expressed in terms of latitudes andlongitudes and also a time-to-live (TTL) parameter specifying a timeperiod after which the route data can be deleted. Accordingly, theadd-planned-routes API allows the vehicle to indicate the route thevehicle is planning on taking in the near future as an autonomous trip.The add-planned-route API aligns the route to the HD map, records theroute and its TTL value, and makes sure that the HD map data for theroute stored in the vehicle computing system 120 is up to date. Theget-planned-routes API returns a list of planned routes and providesinformation describing a route identified by a route identifier.

The map update API 285 manages operations related to update of map data,both for the local HD map store 275 and for the HD map store 165 storedin the online HD map system 110. Accordingly, modules in the vehiclecomputing system 120 invoke the map update API 285 for downloading datafrom the online HD map system 110 to the vehicle computing system 120for storing in the local HD map store 275 as necessary. The map updateAPI 285 also allows the vehicle computing system 120 to determinewhether the information monitored by the vehicle sensors 105 indicates adiscrepancy in the map information provided by the online HD map system110 and uploads data to the online HD map system 110 that may result inthe online HD map system 110 updating the map data stored in the HD mapstore 165 that is provided to other vehicles 150.

FIG. 4 illustrates the various layers of instructions in the HD Map APIof a vehicle computing system, according to an embodiment. Differentmanufacturer of vehicles have different instructions for receivinginformation from vehicle sensors 105 and for controlling the vehiclecontrols 130. Furthermore, different vendors provide different computeplatforms with autonomous driving capabilities, for example, collectionand analysis of vehicle sensor data. Examples of compute platform forautonomous vehicles include platforms provided vendors, such as NVIDIA,QUALCOMM, and INTEL. These platforms provide functionality for use byautonomous vehicle manufacturers in manufacture of autonomous vehicles.A vehicle manufacturer can use any one or several compute platforms forautonomous vehicles. The online HD map system 110 provides a library forprocessing HD maps based on instructions specific to the manufacturer ofthe vehicle and instructions specific to a vendor specific platform ofthe vehicle. The library provides access to the HD map data and allowsthe vehicle to interact with the online HD map system 110.

As shown in FIG. 3, in an embodiment, the HD map API is implemented as alibrary that includes a vehicle manufacturer adapter 310, a computeplatform adapter 320, and a common HD map API layer 330. The common HDmap API layer comprises generic instructions that can be used across aplurality of vehicle compute platforms and vehicle manufacturers. Thecompute platform adapter 320 include instructions that are specific toeach computer platform. For example, the common HD Map API layer 330 mayinvoke the compute platform adapter 320 to receive data from sensorssupported by a specific compute platform. The vehicle manufactureradapter 310 comprises instructions specific to a vehicle manufacturer.For example, the common HD map API layer 330 may invoke functionalityprovided by the vehicle manufacturer adapter 310 to send specificcontrol instructions to the vehicle controls 130.

The online HD map system 110 stores compute platform adapters 320 for aplurality of compute platforms and vehicle manufacturer adapters 310 fora plurality of vehicle manufacturers. The online HD map system 110determines the particular vehicle manufacturer and the particularcompute platform for a specific autonomous vehicle. The online HD mapsystem 110 selects the vehicle manufacturer adapter 310 for theparticular vehicle manufacturer and the compute platform adapter 320 theparticular compute platform of that specific vehicle. The online HD mapsystem 110 sends instructions of the selected vehicle manufactureradapter 310 and the selected compute platform adapter 320 to the vehiclecomputing system 120 of that specific autonomous vehicle. The vehiclecomputing system 120 of that specific autonomous vehicle installs thereceived vehicle manufacturer adapter 310 and the compute platformadapter 320. The vehicle computing system 120 periodically checks if theonline HD map system 110 has an update to the installed vehiclemanufacturer adapter 310 and the compute platform adapter 320. If a morerecent update is available compared to the version installed on thevehicle, the vehicle computing system 120 requests and receives thelatest update and installs it.

HD Map System Architecture

FIG. 4 shows the system architecture of an HD map system, according toan embodiment. The online HD map system 110 comprises a map creationmodule 410, a map update module 420, a map data encoding module 430, aload balancing module 440, a map accuracy management module, a vehicleinterface module, and a HD map store 165. Other embodiments of online HDmap system 110 may include more or fewer modules than shown in FIG. 4.Functionality indicated as being performed by a particular module may beimplemented by other modules. In an embodiment, the online HD map system110 may be a distributed system comprising a plurality of processors.

The map creation module 410 creates the map from map data collected fromseveral vehicles that are driving along various routes. The map updatemodule 420 updates previously computed map data by receiving more recentinformation from vehicles that recently traveled along routes on whichmap information changed. For example, if certain road signs have changedor lane information has changed as a result of construction in a region,the map update module 420 updates the maps accordingly. The map dataencoding module 430 encodes map data to be able to store the dataefficiently as well as send the required map data to vehicles 150efficiently. The load balancing module 440 balances load across vehiclesto ensure that requests to receive data from vehicles are uniformlydistributed across different vehicles. The map accuracy managementmodule 450 maintains high accuracy of the map data using varioustechniques even though the information received from individual vehiclesmay not have high accuracy.

FIG. 5 illustrates the components of an HD map, according to anembodiment. The HD map comprises maps of several geographical regions.The HD map 510 of a geographical region comprises a landmark map (LMap)520 and an occupancy map (OMap) 530. The landmark map comprisesinformation describing lanes including spatial location of lanes andsemantic information about each lane. The spatial location of a lanecomprises the geometric location in latitude, longitude and elevation athigh prevision, for example, at or below 10 cm precision. The semanticinformation of a lane comprises restrictions such as direction, speed,type of lane (for example, a lane for going straight, a left turn lane,a right turn lane, an exit lane, and the like), restriction on crossingto the left, connectivity to other lanes and so on. The landmark map mayfurther comprise information describing stop lines, yield lines, spatiallocation of crosswalks, safely navigable space, spatial location ofspeed bumps, curb, and road signs comprising spatial location and typeof all signage that is relevant to driving restrictions. Examples ofroad signs described in an HD map include stop signs, traffic lights,speed limits, one-way, do-not-enter, yield (vehicle, pedestrian,animal), and so on.

The occupancy map 530 comprises spatial 3-dimensional (3D)representation of the road and all physical objects around the road. Thedata stored in an occupancy map 530 is also referred to herein asoccupancy grid data. The 3D representation may be associated with aconfidence score indicative of a likelihood of the object existing atthe location. The occupancy map 530 may be represented in a number ofother ways. In one embodiment, the occupancy map 530 is represented as a3D mesh geometry (collection of triangles) which covers the surfaces. Inanother embodiment, the occupancy map 530 is represented as a collectionof 3D points which cover the surfaces. In another embodiment, theoccupancy map 530 is represented using a 3D volumetric grid of cells at5-10 cm resolution. Each cell indicates whether or not a surface existsat that cell, and if the surface exists, a direction along which thesurface is oriented.

The occupancy map 530 may take a large amount of storage space comparedto a landmark map 520. For example, data of 1 GB/Mile may be used by anoccupancy map 530, resulting in the map of the United States (including4 million miles of road) occupying 4×10¹⁵ bytes or 4 petabytes.Therefore the online HD map system 110 and the vehicle computing system120 use data compression techniques for being able to store and transfermap data thereby reducing storage and transmission costs. Accordingly,the techniques disclosed herein make self-driving of autonomous vehiclespossible.

In one embodiment, the HD Map does not require or rely on data typicallyincluded in maps, such as addresses, road names, ability to geo-code anaddress, and ability to compute routes between place names or addresses.The vehicle computing system 120 or the online HD map system 110accesses other map systems, for example, GOOGLE MAPs to obtain thisinformation. Accordingly, a vehicle computing system 120 or the onlineHD map system 110 receives navigation instructions from a tool such asGOOGLE MAPs into a route and converts the information to a route basedon the HD map information.

Geographical Regions in HD Maps

The online HD map system 110 divides a large physical area intogeographical regions and stores a representation of each geographicalregion. Each geographical region represents a contiguous area bounded bya geometric shape, for example, a rectangle or square. In an embodiment,the online HD map system 110 divides a physical area into geographicalregions of the same size independent of the amount of data required tostore the representation of each geographical region. In anotherembodiment, the online HD map system 110 divides a physical area intogeographical regions of different sizes, where the size of eachgeographical region is determined based on the amount of informationneeded for representing the geographical region. For example, ageographical region representing a densely populated area with a largenumber of streets represents a smaller physical area compared to ageographical region representing sparsely populated area with very fewstreets. Accordingly, in this embodiment, the online HD map system 110determines the size of a geographical region based on an estimate of anamount of information required to store the various elements of thephysical area relevant for an HD map.

In an embodiment, the online HD map system 110 represents a geographicregion using an object or a data record that comprises variousattributes including, a unique identifier for the geographical region, aunique name for the geographical region, description of the boundary ofthe geographical region, for example, using a bounding box of latitudeand longitude coordinates, and a collection of landmark features andoccupancy grid data.

FIGS. 6A-B illustrate geographical regions defined in an HD map,according to an embodiment. FIG. 6A shows a square geographical region610 a. FIG. 6B shows two neighboring geographical regions 610 a and 610b. The online HD map system 110 stores data in a representation of ageographical region that allows for smooth transition from onegeographical region to another as a vehicle drives across geographicalregion boundaries.

According to an embodiment, as illustrated in FIG. 6, each geographicregion has a buffer of a predetermined width around it. The buffercomprises redundant map data around all 4 sides of a geographic region(in the case that the geographic region is bounded by a rectangle). FIG.6A shows a boundary 620 for a buffer of 50 meters around the geographicregion 610 a and a boundary 630 for buffer of 100 meters around thegeographic region 610 a. The vehicle computing system 120 switches thecurrent geographical region of a vehicle from one geographical region tothe neighboring geographical region when the vehicle crosses a thresholddistance within this buffer. For example, as shown in FIG. 6B, a vehiclestarts at location 650 a in the geographical region 610 a. The vehicletraverses along a route to reach a location 650 b where it cross theboundary of the geographical region 610 but stays within the boundary620 of the buffer. Accordingly, the vehicle computing system 120continues to use the geographical region 610 a as the currentgeographical region of the vehicle. Once the vehicle crosses theboundary 620 of the buffer at location 650 c, the vehicle computingsystem 120 switches the current geographical region of the vehicle togeographical region 610 b from 610 a. The use of a buffer prevents rapidswitching of the current geographical region of a vehicle as a result ofthe vehicle travelling along a route that closely tracks a boundary of ageographical region.

Lane Representations in HD Maps

The HD map system 100 represents lane information of streets in HD maps.Although the embodiments described herein refer to streets, thetechniques are applicable to highways, alleys, avenues, boulevards, orany other path on which vehicles can travel. The HD map system 100 useslanes as a reference frame for purposes of routing and for localizationof a vehicle. The lanes represented by the HD map system 100 includelanes that are explicitly marked, for example, white and yellow stripedlanes, lanes that are implicit, for example, on a country road with nolines or curbs but two directions of travel, and implicit paths that actas lanes, for example, the path that a turning car makes when entering alane from another lane. The HD map system 100 also stores informationrelative to lanes, for example, landmark features such as road signs andtraffic lights relative to the lanes, occupancy grids relative to thelanes for obstacle detection, and navigable spaces relative to the lanesso the vehicle can efficiently plan/react in emergencies when thevehicle must make an unplanned move out of the lane. Accordingly, the HDmap system 100 stores a representation of a network of lanes to allow avehicle to plan a legal path between a source and a destination and toadd a frame of reference for real time sensing and control of thevehicle. The HD map system 100 stores information and provides APIs thatallow a vehicle to determine the lane that the vehicle is currently in,the precise vehicle location relative to the lane geometry, and allrelevant features/data relative to the lane and adjoining and connectedlanes.

FIG. 7 illustrates lane representations in an HD map, according to anembodiment. FIG. 7 shows a vehicle 710 at a traffic intersection. The HDmap system provides the vehicle with access to the map data that isrelevant for autonomous driving of the vehicle. This includes, forexample, features 720 a and 720 b that are associated with the lane butmay not be the closest features to the vehicle. Therefore, the HD mapsystem 100 stores a lane-centric representation of data that representsthe relationship of the lane to the feature so that the vehicle canefficiently extract the features given a lane.

The HD map system 100 represents portions of the lanes as lane elements.A lane element specifies the boundaries of the lane and variousconstraints including the legal direction in which a vehicle can travelwithin the lane element, the speed with which the vehicle can drivewithin the lane element, whether the lane element is for left turn only,or right turn only, and so on. The HD map system 100 represents a laneelement as a continuous geometric portion of a single vehicle lane. TheHD map system 100 stores objects or data structures representing laneelements that comprise information representing geometric boundaries ofthe lanes; driving direction along the lane; vehicle restriction fordriving in the lane, for example, speed limit, relationships withconnecting lanes including incoming and outgoing lanes; a terminationrestriction, for example, whether the lane ends at a stop line, a yieldsign, or a speed bump; and relationships with road features that arerelevant for autonomous driving, for example, traffic light locations,road sign locations and so on.

Examples of lane elements represented by the HD map system 100 include,a piece of a right lane on a freeway, a piece of a lane on a road, aleft turn lane, the turn from a left turn lane into another lane, amerge lane from an on-ramp an exit lane on an off-ramp, and a driveway.The HD map system 100 represents a one lane road using two laneelements, one for each direction. The HD map system 100 representsmedian turn lanes that are shared similar to a one-lane road.

FIGS. 8A-B illustrates lane elements and relations between lane elementsin an HD map, according to an embodiment. FIG. 8A shows an example of aT junction in a road illustrating a lane element 810 a that is connectedto lane element 810 c via a turn lane 810 b and is connected to lane 810e via a turn lane 810 d. FIG. 8B shows an example of a Y junction in aroad showing label 810 f connected to lane 810 h directly and connectedto lane 810 i via lane 810 g. The HD map system 100 determines a routefrom a source location to a destination location as a sequence ofconnected lane elements that can be traversed to reach from the sourcelocation to the destination location.

Coordinate Systems

FIGS. 9A-B illustrate coordinate systems for use by the HD map system,according to an embodiment. Other embodiments can use other coordinatesystems.

In an embodiment, the HD map system uses a vehicle coordinate systemillustrated in FIG. 9A such that the positive direction of the X-axis isforward facing direction of the vehicle, the positive direction of theY-axis is to the left of the vehicle when facing forward, and thepositive direction of the Z-axis is upward. All axes represent distance,for example, using meters. The origin of the coordinate system is on theground near center of car such that the z-coordinate value of the originat the ground level, and x-coordinate and y-coordinate values are nearcenter of the car. In an embodiment the X, Y coordinates of the originalat the center of LIDAR sensor of the car.

In another embodiment, the HD map system uses a LIDAR coordinate systemillustrated in FIG. 9B such that the positive direction of the X-axis isto the left of the vehicle when facing forward, the positive directionof the Y-axis is in the forward direction of the vehicle, and thepositive direction of the Z-axis is upward. All axes represent distance,for example, using meters. The origin of the coordinate system is at thephysical center of the puck device of the LIDAR.

During a calibration phase the HD map system determines the coordinatetransform T_(l2c) to map the LIDAR coordinate system to the vehiclecoordinate system. For example, given a point P_(lidar), thecorresponding point in the vehicle P_(car) may be obtained by performingthe transformation P_(car)=T_(l2c)*P_(lidar). The point representationP_(car) is used for alignment and processing.

Point Cloud Unwinding

FIG. 10 illustrates the process of LIDAR point cloud unwinding by the HDmap system, according to an embodiment. The LiDAR is mounted on a movingvehicle. Accordingly, the LIDAR is moving while it takes a scan. Forexample, with 65 mile per hour traveling speed, a LIDAR sampling at 10HZ can travel up to 3.5 m during each scan. The HD map systemcompensates for the motion of the LIDAR to transform the raw LIDAR scandata to a point cloud that is consistent with the real world.

To recover the true 3D point cloud of the surrounding environmentrelative to the LiDAR is location at a specific timestamp, the HD mapsystem performs a process referred to as unwinding to compensate theLiDAR's motion during the course of scanning the environment.

Assume the motion the LiDAR moved during the scan as T. The LiDAR beamsare identified via their row and column index in the range image. The HDmap system derives the relative timing of each LiDAR beam relative tothe starting time of the scan. The HD map system uses a linear motioninterpolation to move each LiDAR beam according to its interpolatedmotion relative to the starting time. After adding this additionalmotion compensation to each LiDAR beam, the HD map system recovers thestatic world environment as an unwound point cloud.

According to different embodiments, there are different ways to estimatethe LiDAR's relative motion (T), i.e., the unwinding transform, duringthe course of each scan. In one embodiment, the HD map system usesGNSS-IMU (global positioning system—inertial measurement unit) data forunwinding. In another embodiment, the HD map system runs a pairwisepoint cloud registration using raw, consecutive LiDAR point clouds. Inanother embodiment, the HD map system perform global alignment, and thencomputes the relative transform from the adjacent LiDAR poses.

Global Alignment

Given a collection of tracks (which includes GNSS-IMU and LiDAR data),the HD map system performs global alignment that fuses the GNSS-IMU andLiDAR data to compute globally consistent vehicle poses (location andorientation) for each LiDAR frame. With the global vehicle poses, the HDmap system merges the LiDAR frames as a consistent, unified point cloud,from which a 3D HD map can be built.

FIG. 11 shows the system architecture of the global alignment moduleaccording to an embodiment. The global alignment module includes a posegraph update module 1110, a pairwise alignment module 1120, an ICPresult filter module 1130, a surface classification module 1140, a posegraph optimization module 1150, a distributed execution module 1160, amisalignment hotspot detection modules 1160, a GNSS pose priorprocessing module 1170, and a pose graph store 1180. The functionalityof each module is further described in connection with the variousprocesses described herein.

Single Track Pairwise Alignment

FIG. 12(A) illustrate single track pairwise alignment process accordingto an embodiment. In an embodiment, the single pairwise alignmentprocess is executed by the pairwise alignment module 1120. The HD mapsystem organizes each data collection data as a track. The track dataincludes at least GNSS-IMU and LiDAR data. The single track alignment isa preprocessing step that performs the following steps: (1) Receiving1205 single track data (1) Performing 1210 LiDAR to vehicle calibrationtransform; (2) Performing 1215 synchronization of GNSS-IMU and LiDARdata based on their timestamps; (3) Remove 1220 stationary samples, forexample, samples taken when the car is stopped at a traffic light; (4)Computing 1225 unwinding transforms for motion compensation; (5)Performing 1230 pairwise alignment by computing pairwise registrationsbetween point clouds within the same track. The HD map system alsoperforms 1235 an ICP result filter.

When the vehicle stops, e.g., at a red traffic light, the LiDARmeasurements are redundant. The HD map system removes the stationarypoint cloud samples by prefiltering the track samples. The HD map systempreserves the first sample for each track. The HD map system identifiessubsequent samples as non-stationary samples if and only if theirdistance to the previous non-stationary sample measured by their GNSSlocations exceeds a certain threshold measured in meters (e.g., 0.1meter). [0002] Though GNSS measurements are not always accurate, andcould have sudden jumps, the HD map system uses GNSS locations to filterout stationary samples because GNSS measurements are globallyconsistent. As a comparison, the relative locations from global LiDARposes can vary each time a global optimization is computed, resulting inunstable non-stationary samples.

Unwinding transforms are the relative motions during the course of thescans (i.e., from the starting time to the end time of the scan).Therefore, the HD map system always uses consecutive LiDAR samples toestimate the unwinding transforms. For example, the HD map system needsto compute the unwinding transform for a non-stationary sample i, the HDmap system uses the immediate sample (i+1) to compute this unwindingtransform, even if sample (i+1) may not be a non-stationary sample.

The HD map system precomputes the unwinding transforms using raw LiDARpoint clouds. The basic assumption is the correct motion can beestimated by running point-to-plane ICP using just the raw LiDAR pointclouds. This assumption is generally true for steady motion (i.e., nochange in velocity or rotation rate). Under this assumption, theunwinding transforms have the same effect for both the related pointclouds, therefore, ignoring it still provides a reasonable motionestimation from the two raw point clouds.

To compute the unwinding transforms for non-stationary sample i, the HDmap system finds its consecutive LiDAR sample (i+1), and runpoint-to-plane ICP using the following settings: (1) Source point cloud:LiDAR sample i+1 (2) Target point cloud: LiDAR sample i. The HD mapsystem estimates normals and constructs a spatial indexing datastructure (KD-tree) for the target point cloud, and computes therelative transform of the source point cloud using the point-to-planeICP as the unwinding transform. For the initial guess of the ICPprocess, the HD map system uses the motion estimation from GNSS-IMU. TheICPs to compute unwinding transforms may fail to converge. In suchcases, the HD map system ignores the non-stationary samples.

Once the unwinding transforms for all non-stationary samples arecomputed, the HD map system uses the unwinding transforms tomotion-compensate all related point clouds so that they are consistentwith the real world. The HD map system computes pairwise point cloudalignments between same-track non-stationary samples. For eachnon-stationary track sample, the HD map system uses a search radius tofind other nearby single-track non-stationary samples. The HD map systemorganizes the related samples into a list of ICP pairs for ICPcomputation. Each ICP pair can potentially be computed independently viaa parallel computing framework. For each ICP pair, the source and targetpoint clouds are first unwound using their corresponding unwindingtransforms, then provided as input to a point-to-plane ICP process toget their relative transforms.

The pairwise point-to-plane ICP process computes a transformation matrix(T), and also reports the confidence of the 6 DOF (degrees of freedom)transformation as a 6×6 information matrix. For example, if the 6-DOFmotion estimation is [tx, ty, tz, roll, pitch, yaw], the 6×6 informationmatrix (Ω) is the inverse of the covariance matrix, which providesconfidence measures for each dimension. For example, if the car enters along corridor along x axis, the point-to-plane ICP cannot accuratelydetermine the relative motion along the x axis, therefore, the HD mapsystem assigns the element in the information matrix corresponding to txa low confidence value (i.e., large variance for tx).

Cross Track Pairwise Alignment

In order to merge LiDAR samples from different tracks, the HD map systemcomputes loop closing pairwise transforms for LiDAR samples fromdifferent tracks. For each non-stationary sample, the HD map systemsearches within a radius for nearby non-stationary samples from othertracks. The related samples are organized into a list of ICP pairs forICP computation. Each ICP pair can be computed independently via aparallel computing framework. For each ICP pair, the source and targetpoint clouds are first unwound using their corresponding unwindingtransforms, then fed to point-to-plane ICP to get their relativetransforms.

Upon completing all pairwise alignments, the HD map system performsglobal pose optimization and solves the following problem.

Given a set of N samples from multiple tracks {Sample_(i)} and theirrelative transformations among these samples {T_(ij), iϵ[1 . . . N],jϵ[1 . . . N]} and the corresponding information matrices {Ω_(ij), iϵ[1. . . N], jϵ[1 . . . N]}, the HD map system computes a set of globalconsistent poses for each sample {x_(i), iϵ[1 . . . N]}, so that theinconsistency between pairwise transformations are minimized:

$\min\limits_{x_{i}}{\sum\limits_{ij}{\left\lbrack {{T\left( {x_{i}^{- 1} \cdot x_{j}} \right)}^{- 1} \cdot T_{ij}} \right\rbrack^{T} \cdot \Omega_{ij} \cdot \left\lbrack {{T\left( {x_{i}^{- 1} \cdot x_{j}} \right)}^{- 1} \cdot T_{ij}} \right\rbrack}}$

FIG. 12(B) illustrate global alignment process according to anembodiment. Upon completion of single track alignments, the globalalignment module 400 performs global alignment by performing 1245cross-track pairwise alignment for LiDAR samples from different tracksfor loop closing purposes. The HD map system combines the single trackpairwise alignment results and the cross track pairwise alignmentresults, to build a global pose graph. The HD map system repeatedlyperforms 1255 global pose optimization and performs 1260 review andmanual improvements of the results. The HD map system determines 1270the final vehicle poses via global pose graph optimization.

Generating Pose Graph

In an embodiment, the global optimization is performed as a processingof a pose graph. The HD map system uses a node to represent the pose ofeach sample for all the samples available ({V_(i)=x_(i)}). The edges arepairwise transformations and the corresponding ({E_(ij)={T_(ij),Ω_(ij)}}) information matrices for pairwise transforms among the nodes.

FIG. 13 shows a visualization of a pose graph according to anembodiment. The goal of global pose optimization is to optimize the posefor each node in the pose graph, such that their relative pairwisetransformations are as close to the ones computed from pairwise ICP aspossible, weighted by the corresponding information matrices. This canbe expressed mathematically using the following equation:

$\min\limits_{x_{i}}{\sum\limits_{ij}{\left\lbrack {{T\left( {x_{i}^{- 1} \cdot x_{j}} \right)}^{- 1} \cdot T_{ij}} \right\rbrack^{T} \cdot \Omega_{ij} \cdot \left\lbrack {{T\left( {x_{i}^{- 1} \cdot x_{j}} \right)}^{- 1} \cdot T_{ij}} \right\rbrack}}$

where, T(x_(i) ⁻¹∘x_(j)) is the pairwise transform between node i andnode j, computed from the global poses; T(x_(i) ⁻¹∘x_(j))⁻¹∘T_(ij) isthe difference between the pairwise transforms computed from globalposes compared to the one computed from ICP; and [T(x_(i)⁻¹∘x_(j))⁻¹∘T_(ij)]^(T)·Ω_(ij)·[T(x_(i) ⁻¹∘x_(j))⁻¹∘T_(ij)] is the errorterm from edge e_(ij) due to the inconsistency between global poses andthe pairwise ICP weighted by the information matrix Ω_(ij). The moreconfident of the ICP results, the “larger” Ω_(ij) would be, andtherefore the higher the error term.

Adding Pose Priors to Pose Graph

The pose graph optimization above adds constraints due to the pairwisetransforms, and therefore, any global transformations to all nodes arestill valid solutions to the optimization. In an embodiment, the HD mapsystem keeps the poses consistent with GNSS measurements as close aspossible, since the GNSS measurements are generally consistent globally.Therefore, the HD map system selects a subset of nodes (P), andminimizes its global pose differences with their corresponding GNSSposes.

FIG. 14 shows a visualization of a pose graph that includes pose priorsbased on GNSS, according to an embodiment. As shown in FIG. 14, unaryedges are added to a subset of the nodes in the pose graph. The dots1410 illustrated as pink nodes are pose priors added to thecorresponding graph nodes.

Adding global pose priors is equivalent to adding regularization termson the subset of nodes were: x_(i): is the global GNSS pose prior addedto node x_(i), and T(x_(i) ⁻¹ x _(i)) is the pose difference betweencurrent pose x_(i), and its pose prior x_(i); Ω_(ii): is the strength,or confidence of the global pose prior. The larger this information is,the more weight is added for this global pose prior. According tovarious embodiments, there are multiple ways to select the subset ofsamples for pose prior: (1.) Select samples from each track at fixeddistance interval (2.) Perform random sampling (3.) Select one node perlatitude/longitude bounding box Embodiments may adapt the creation ofnode subsets based on the quality of the GNSS poses and pairwisealignments. For example, the selection process may favor selection ofGNSS nodes with high quality pose or increase the sampling density inregions with low quality pairwise alignments.

The HD map system determines confidence for the global pose priorssimilar to the confidence measures of the GNSS poses. The confidence canbe measured with the inverse of estimated error variance. Accordingly,the HD map system associates lower variance with a higher confidence inthe pose. The HD map system determines a position error variance whileperforming the GNSS calculations used to estimate position. The HD mapsystem determines a position error variance by taking into considerationfactors like the relative positions of the satellites and residualerrors in distance to each satellite. The HD map system determines fullpose (position and orientation) error variance as a by-product of thecalculations for combining the GNSS poses with other sensor data (forexample, IMU, magnetometer, visual odometry, etc.). In an embodiment,the HD map system uses a Kalman filter that outputs error variance withevery estimate of pose. The HD map system determines that the better theestimated pose agrees with the GNSS and IMU data, the lower the expectederror variance and the higher our confidence is in the pose. Bothposition and pose error variances are standard outputs of integratedGNSS/IMU devices.

Pose Graph Optimization

The HD map system performs initialization of the pose graph for correctconvergence. The pose graph optimization is a nonlinear optimizationwhere multiple local minima could exist. The HD map system uses GNSSposes to initialize all the poses in the pose graph. Although each GNSSpose could be quite off, the GNSS measurement provides a global bound onthe pose error. After initializing the pose graph, the HD map systemoptimizes the entire pose graph using non-linear solvers.

The automatic constructed pose graphs may prone to errors due to lack ofloop closing edges, or non-planar structures such as overpasses,multilevel garages, etc. The HD map system assigns operators to verifythe globally optimized poses by checking the sharpness of the mergedpoint clouds with global poses. The HD map system also allows theoperators to manually add loop closing edges to improve the pose graphquality. Upon completion of the manual review process, the new posegraph with added manual edges are optimized so that a more accurate setof global poses can be produced. To automatically identify where the HDmap system needs manual review or improvements, the HD map systemprovides automatic alignment hot spot detection.

Overall Process of Alignment

FIG. 15 shows a flowchart illustrating the process for performing posegraph optimization, according to an embodiment. Various steps of theprocess may be performed by the pose graph optimization module 1150 andGNSS pose prior processing module 1170. The HD map system stores 1500 apose graph in the pose graph store 1180. A pose represents the locationand orientation of a vehicle. The HD map system collects track data,each track data comprising sensor data collected by vehicles drivingalong a route, for example, LIDAR frames representing range imagescollected by LIDARs mounted on autonomous vehicles. An edge between twoposes in a pose graph connects two associated nodes, for example, nodesthat represent consecutive locations from where sensor data wascollected by a vehicle along a route.

The GNSS pose prior processing module 1170 selects 1510 a subset ofnodes from the pose graph. For each node of the subset, the GNSS poseprior processing module 1170 performs the steps 1520 and 1530. The GNSSpose prior processing module 1170 identifies 1520 a GNSS posecorresponding to the node. The GNSS pose prior processing module 1170minimizes 1530 global pose difference between the node and the GNSSpose.

The pose graph optimization module 1150 performs 1540 pose graphoptimization using the pose graph. The HD map system merges the sensordata, e.g., the LIDAR frames to generate a consistent, unified pointcloud. The HD map system generates the high definition map using thepoint cloud.

Filtering of ICP Results

Conventional ICP techniques do not guarantee converging to the globaloptimal solutions. In practice, if the initial guess to the ICPalgorithm is bad, ICP can get stuck at local minima and return incorrecttransforms. Therefore, embodiments use a quality control method to rateeach ICP results, and remove bad ICP results. The HD map system uses anautomatic QA method for ICP results based on various statistics that canbe collected during ICP computation.

At the last iteration of each ICP, the HD map system collects thestatistics from the current set of correspondencesC={(c_(i)|c_(i)=[s_(i)→(d_(i),n_(i))]}. From these correspondences, theHD map system collects the following statistics: (1) Average signeddistance error among correspondences. (2) Variance of the signedpoint-to-plane errors among correspondences. (3) Histogram of thedistribution of signed distance error among correspondences. Since thetransformation is in 6D parameter space (three degrees of freedom (3DOF)for translation, and 3DOF for rotation), the HD map system approximatesthe cost function as a quadratic cost as shown by the followingequation:

${\min\limits_{T}{\sum\limits_{i}{w_{i}{{n_{i} \cdot \left( {{T \cdot s_{i}} - d_{i}} \right)}}^{2}}}} = {x_{T}^{\prime} \cdot \Omega \cdot x_{T}}$

Where x_(T)=[t_(x), t_(y), t_(z), roll, pitch, yaw] is the 6D vectorthat can generate the 4×4 transformation matrix, and is called ICPinformation matrix. The information matrix reveals the uncertainties foreach dimension of the ICP optimization. For ICP information matrix, theHD map system computes the following attributes: conditional number andEigen values.

In addition to the statistics, the HD map system also computes the posedifferences between ICP results and the corresponding GNSS-IMU pose. Foreach ICP result, the HD map system computes the pose difference betweenthe ICP result and the pairwise pose computed from GNSS-IMU posedifference between the ICP result and the pairwise pose computed fromGNSS-IMU. The pose difference can be summarized as a 6D vector:[delta_x, delta_y, delta_z, delta_roll, delta_pitch, delta_yaw]. This 6Dvector is also added to the feature vector for SVM ICP resultclassifier.

Combining the statistics and analysis of the ICP information matrix, theHD map system forms a feature vector for each ICP including followingfeatures: Average signed distance error, Variance signed distance error,Error histogram, Conditional number, Eigen values, Difference betweenGNSS-IMU pose, and other possible features, e.g., difference betweenvisual odometry. The HD map system first builds a set of human verifiedground truth ICP results dataset, and computes a feature vector for eachICP result. This collection of feature vectors allows the HD map systemto train a binary classifier, e.g., an SVM classifier, which can predictthe probability of current ICP result being correct according to thecorresponding feature vector. The trained machine learning model (e.g.,SVM model), reports a probability of each ICP result being correct,which allows the HD map system to filter out bad ICP results, and informhuman labelers of bad ICP results that may need manual adjustment.

FIG. 16 shows the system architecture of a machine learning based ICPResult filter, according to an embodiment. The ICP result filter 480comprises a training module 1610, an ICP result filter model 1620, afeature extraction module 1630, and a training data store 1640. Thetraining data store 1640 stores training data that is used for trainingthe ICP result filter model 1620. The feature extraction module 1630extracts features from either data stored in training data store 1640 orfrom input data that is received for processing using the ICP resultfilter model 1620. The training module 1610 trains an ICP result filtermodel 1620 using the training data stored in the training data store1640.

FIG. 17 shows a process for training a model for machine learning basedICP result filter, according to an embodiment. The training module 1610receive 1700 a plurality of datasets comprising 3D representations ofregions determined from data captured by sensors of vehicles.

The training module 1610 repeats the steps 1610, 1620, 1630, 1640, and1650. The training module 1610 determines 1710 a first 3D representationand a second 3D representation of a plurality of objects in ageographical region. The training module 1610 determines 1720 atransformation to map the first 3D representation to the second 3Drepresentation based on iterative closest point (ICP) technique. Thefeature extraction module 1630 extracts 1730 a feature vector comprisingfeatures based on a particular ICP result. The training module 1610receives 1750 a label from a user for the ICP result. The trainingmodule 1610 stores the labeled ICP result in the training data store1740.

The training module 1610 train a machine learning based ICP resultfilter model 1620 configured to generate a score indicating whether aninput ICP result needs manual verification.

FIG. 18 shows a process for performing ICP result filter using a machinelearning based model, according to an embodiment. The ICP result filtermodule 480 receives 1800 sensor data for a portion of a geographicalregion captured by sensors of autonomous vehicles driving in thatregion.

The ICP result filter module 480 repeats the steps 1810, 1820, 1830,1840, and 1850 to perform global pose optimization. The ICP resultfilter module 480 determines 1810 a first 3D representation and a second3D representation of a plurality of objects in a portion of the region.The ICP result filter module 480 determines 1820 a transformation to mapthe first 3D representation to the second 3D representation based oniterative closest point (ICP) technique. The ICP result filter module480 extracts 1830 a feature vector comprising features based on the ICPresults. The ICP result filter module 480 provides 1840 the featurevector as input to the machine learning based ICP result filter model1820 for determining a score indicative of correctness of the ICPresult. If the score indicates that the result is inaccurate, the ICPresult filter module 480 provides the result for manual verification. Insome embodiments, the result verification is performed by an automaticagent, for example, by an expert system.

The HD map system receives the results based on manual verification andgenerates 1860 the HD map based on global pose optimization.

Distributed Execution of Global Alignment

For creating maps covering a large geographical region, for example, alarge city, the pose graph may contain billions of samples with hugenumber of edges. It is practically not possible to perform the posegraph optimization on a single computing machine. Embodiments of the HDmap system implement a distributed method to optimize large pose graphs.

FIG. 19(A-B) illustrate division of a pose graph into subgraphs fordistributed execution of the pose graph optimization, according to anembodiment. As illustrated in FIG. 19(A), given a large pose graph 1900,the HD map system divides the pose graph into disjoint sub graphs 1910.For each sub graph 1910, the HD map system extends its boundary withsome margin, as shown in FIG. 19. As a result, each sub graph contains aset of core nodes 1920, which are nodes processed by this specific subgraph. In addition, it also has a surrounding buffer nodes 1930 in thebuffer region. On the boundary of the buffer region, the HD map systemhas boundary nodes that are fixed.

As illustrated in FIG. 19(B), the HD map system divides the entire posegraph 1900 into a large number of sub graphs 1910, where the union ofthe core nodes 1920 of all subgraphs covers all nodes in original posegraph. The following processes optimize the pose graph in a distributedfashion.

FIG. 20 shows a process for distributed processing of a pose graph,according to an embodiment. In an embodiment, steps of this process areexecuted by the distributed execution module 1160. The HD map systemreceives 2000 nodes and edges of a pose graph. A node in the pose graphrepresents a pose of a sample, and an edge in the pose graph representsa pairwise transformation between the nodes of a pair. The distributedexecution module 1160 divides 2010 the pose graph into a plurality ofpose subgraphs, each pose subgraph comprising a core pose subgraphportion and a boundary pose subgraph portion as illustrated in FIG. 19.The distributed execution module 1160 distributes 2020 the subgraphsacross a plurality of processors, each processor assigned a set ofneighboring pose subgraphs. The HD map system 100 performs 2030 globalpose graph optimization in parallel using the plurality of processors.

FIG. 21 shows a process for distributed optimization of a pose graph,according to an embodiment. The HD map system repeats the followingsteps for each pose subgraph. The following steps are repeated whileboundary node poses change across iterations or while an aggregatemeasure of changes across boundary nodes is greater than a thresholdvalue. The HD map system optimizes 2100 pose subgraph while keepingboundary nodes fixed. The HD map system updates 2110 boundary nodes fromneighboring pose subgraphs. The HD map system determines 2120 an amountof change in the boundary nodes. The HD map system marks 2130 subgraphoptimization complete if there is no change in boundary nodes or ameasure of change in boundary nodes is below a threshold value.

The way the HD map system we cuts the pose graph into sub pose graphswith disjoint core nodes affects the convergence speed of the joint posegraph. A sub pose graph is also referred to herein as a pose subgraph.There is a long feedback loop where the errors are bouncing as a wave,leading to boundary node changes for a large number of iterations.Various embodiments. Various embodiments address the problem withdifferent candidate subdivision strategies. According to an embodiment,the HD map system subdivides pose graph based on latitude/longitudebounding boxes. This strategy may lead to large number of boundarynodes, which may slow down the convergence. In another embodiment, theHD map system subdivides the pose graph based on graph cuts. Forexample, the HD map system may cut the pose graphs at the center of longroad sections. This leads to the least number of boundary nodes.

In another embodiment, the HD map system cuts the pose graphs atjunctions. Since samples in junctions often are well constrained by lotsof edges, cutting pose graphs at junctions may result in fastconvergence of the boundary nodes, and thus speed up the distributedpose graph optimization. In general, in an embodiment, the HD map systemidentifies portions of geographical region that have large number ofsamples returned by vehicles and divides the pose graph into subgraphssuch that the boundaries pass through such regions. Having the boundaryof a subgraph pass through portions of geographical regions with largenumber of samples leads to faster convergence.

Incremental Processing of Global Alignment

The HD map system allows incremental addition of data of tracks to apose graph that is constructed and optimized. This allows updates on aregular basis to the pose graph and their incorporation into the HD map.

FIG. 22 shows an example illustrating the process of incremental updatesto a pose graph, according to an embodiment.

FIG. 22 shows an existing pose graph G₀ which includes tracks {Track₁,Track₂, . . . , Track_(N)}. A new set of tracks is added to the graph G0after new tracks being collected, the HD map system adds these M_(new)tracks {Track_(N+1), Track_(N+2), . . . , Track_(N+M)} to the existingpose graph to get a new pose graph G that includes all N+M tracks.

FIG. 23 shows a flowchart illustrating the process of incrementalupdates to a pose graph, according to an embodiment. In an embodiment,various steps of the process are executed by the incremental pose graphupdate module 1110 and performed in the online HD map system 110.

The HD map system generates 2300 a pose graph G₀ including a set S1 oftracks {Track₁, Track₂, . . . , Track_(N)}. In an embodiment, the posegraph G₀ is generated and optimized using the process illustrated inFIG. 20 and FIG. 21. The HD map system receives 2310 a set S2 of newtracks {Track_(N+1), Track_(N+2), . . . , Track_(N+M)} for adding to theexisting pose graph G₀.

The HD map system performs 2320 pairwise alignment across pairs oftracks including tracks Tx and Ty such that both Tx and Ty are selectedfrom the set S2 of M new tracks. Accordingly, the HD map system performssingle track unwinding transforms and single track pairwise transformsfor the new tracks.

The HD map system performs 2330 pairwise alignment across pairs oftracks (Tp, Tq) such that Tp is selected from set S1 and Tq is selectedfrom set S2. Accordingly, the HD map system performs cross trackpairwise alignment, by relating the new tracks with each other, as wellas the new tracks to existing tracks. As shown in FIG. 22, in additionto computing cross track pairwise alignment among the M new tracks, theHD map system also computes cross track pairwise alignments for the newtracks with existing N tracks. With the new single track and cross trackpairwise alignment results, the HD map system constructs a new posegraph G, where the nodes of G are the union of all samples in G₀ and allof the samples in the new tracks. The edges of G include both the edgesfrom G₀ and the newly computed single and cross track pairwise ICPresults.

The HD map system performs 2340 pose graph optimization over tracks fromsets S1 and S2. In one embodiment, the HD map system performsoptimization of the pose graph without changing the poses for theexisting samples for tracks {Track₁, Track₂, . . . , Track_(N)}.Accordingly, the HD map system freezes the poses of the existing samplesfor tracks {Track₁, Track₂, . . . , Track_(N)} during the pose graphoptimization. As a result, the HD map system optimizes only the newtrack sample poses.

In another embodiment, the HD map system makes changes to the existingsample poses for track {Track₁, Track₂, . . . , Track_(N)}. Accordingly,the HD map system starts an entire new global optimization processwithout freezing any of the node poses. In this case, the globaloptimization is similar to a new global optimization for pose graph G.

The HD map system stores 2350 the updated pose graph in the pose graphstore 2280. The HD map system uses the updated pose graph for updatingthe HD map and provides the updated HD map to vehicles driving in theappropriate regions.

Patching of Pose Graph

Due to the quality of the pose graph, the optimized poses generated bythe HD map system may not be accurate for all nodes in the pose graphs.Sometimes, the optimization results may be incorrect in a small regiondue to lack of loop closing edges, or wrong ICP edges being inserted. Inthese cases, the HD map system receives inputs from users, for example,human operators. The received input causes the problematic regions to betagged with a latitude/longitude bounding box. Subsequently the portionof the pose graph inside the latitude/longitude bounding box is fixed,for example, manually.

In an embodiment, the HD map system presents a user interface displayinga portion of the pose graph within a latitude/longitude bounding box.The HD map system receives via the user interface, request to eitherremove edges identified as incorrect or a request to add new verifiededges.

After performing the pose graph edits based on requests received via theuser interface, the HD map system re-optimizes the pose graph andgenerates the correct poses for all the nodes inside the bounding box.In an embodiment, the HD map system does not change the poses outsidethe latitude/longitude bounding box while performing the optimization ofthe edited pose graph. Accordingly, the HD map system freezes the posesof nodes outside latitude/longitude bounding box during the pose graphoptimization. As a result, the HD map system optimizes and updates onlythe sample poses inside the specified latitude/longitude bounding box.In another embodiment, the HD map system starts an entire new globaloptimization without freezing any of the node poses. In this case, theglobal optimization is similar to performing a new global optimizationfor the edited pose graph.

In an embodiment, the HD map system allows deprecating of old tracks.Assuming current pose graph G₀ is optimized, the HD map system simplyremoves the corresponding track samples and all edges connected to them.The result is a smaller pose graph without the deprecated samples. Inthis case, the HD map system does not perform a pose graph optimizationresponsive to deprecating the tracks.

Pairwise Alignment Based on Classification of Surfaces as Hard/Soft

The HD map system computes the relative pairwise transforms between twonearby samples. Given two point clouds, the HD map system usespoint-to-plane ICP (i.e., Iterative Closest Point) process to get theirrelative transformation and the corresponding confidence estimation,represented using an information matrix, which is the inverse of thecovariance matrix.

The HD map system performs the point-to-plane ICP process as follows.The HD map system identifies two point clouds, referred to as source andtarget point cloud, as sparse observations of the surroundingenvironment, and uses the ICP process is to find the desired transformthat transforms the source point cloud from its local coordinate systemto the target point cloud's coordinate system. Due to the sparsity ofthe data, it is unlikely that the source and target point clouds samplethe exact same points in the environment. Therefore, a simplepoint-to-point ICP is prone to error with sparse LiDAR point clouds.Typically, the environments, especially under driving scenarios, oftenhave plenty of planar surfaces. In these situations, usingpoint-to-plane error metric in basic ICP process leads to more robusttransformation estimations.

FIG. 24 illustrates the ICP process performed by the HD map systemaccording to an embodiment. Given correspondences, the HD map systemperforms point-to-plane ICP that minimizes the following cost function:

$\min\limits_{T}{\sum\limits_{i}{w_{i}{{n_{i} \cdot \left( {{T \cdot s_{i}} - d_{i}} \right)}}^{2}}}$

Where (n_(i), d_(i)) is a point in target point cloud, where n_(i) isthe estimated surface normal at point d_(i), and s_(i) is thecorresponding point in source point cloud; w_(i) is the weight assignedto each correspondence, which is set to 1.0 for the simplest weightingstrategy, and may have different adaptations for other weightingschemes. The optimization problem minimizes the sum of point-to-planeerrors from source to target by adjusting the transformation T. In eachiteration, the correspondences are updated via nearest neighborsearches.

The HD map system treats normal estimates for different points withdifferent measures of confidence. For example, the HD map systemassociates the normals of road surface, or building surfaces with highconfidence and normals of tree leaves, or bushes with low confidence,since these aren't stable planar structures. During normal computation,in addition to computing the normal vectors at each location, the HD mapsystem estimates the confidence (or robustness) of each normal byestimating the distribution of neighboring points. The neighboringpoints may be obtained from the LiDAR range image.

FIG. 25 illustrates estimation of point cloud normals on the LiDAR rangeimage by the HD map system, according to an embodiment. FIG. 25illustrates the beams of the LIDAR scan that form a LIDAR point cloud.FIG. 25 also illustrates a corresponding LIDAR range image.

One of the steps performed by the HD map system while performingpoint-to-plane ICP is to reliably estimate the surface normal of thetarget point cloud. In one embodiment, the HD map system estimates thesurface normal at a specific point p via statistical analysis of theneighboring points of p over a certain radius. This is challenging asmost of the LiDAR used in autonomous driving vehicles have limitednumber of laser beams. The collected LiDAR point clouds have higherpoint density for points collected with the same laser beam, and sparserbetween different laser beams. This uneven distribution of pointdensities causes troubles to conventional neighbor search. Accordingly,a smaller search radius makes it difficult to find neighbors within thesame laser beam, which mostly lies on a line instead of a plane. On theother hand, a large search radius that includes neighboring points froma different row may also include points from other planar surfaces.Embodiments of the invention address these problem and allow estimatinga normal for LIDAR range scans.

The HD map system encodes measurement of a LiDAR point cloud as a rangeimage, where the rows and columns encode the pre-calibrated pitch andyaw angles, and the pixel value encodes the distance to the obstacle. Toaddress the challenges of nearest neighbor based normal estimation, theHD map system estimates point cloud normals directly on the LiDAR rangeimage. This technique has the following advantages: (1) More reliable,as it address the point density differences inside each laser beam andacross laser beams. (2) More efficient, since the neighbor search isvery fast on the image grid, and no additional spatial searchingstructure, such as a K-D tree, is needed.

FIG. 26 shows the process for determining point cloud normals on theLiDAR range image by the HD map system, according to an embodiment. TheHD map system receives 2600 LIDAR data captured by a LIDAR mounted on avehicle driving in a particular geographical region. The HD map systemgenerates 2610 a LIDAR range image. In the LIDAR image, the rows andcolumns encode pitch and yaw angles, and the pixel value encodes thedistance of the LIDAR to the obstacle.

For each point P, the HD map system performs the following steps 2630,2640, and 2650. The HD map system determines 2630 the four directneighbors of the data point in the LiDAR range image that are at least aminimum threshold distance away: p_(top), p_(bottom), p_(left), andp_(right). If both, top and bottom neighbors, or left and rightneighbors are missing, HD map system determines that the normal is anempty value.

The HD map system determines 2640 a horizontal and vertical vector fromthe data point based on the direct neighbors. The HD map systemdetermines the most appropriate horizontal and vertical vector ({rightarrow over (v)}_(horizontal), {right arrow over (v)}_(vertical),) from pand its four neighbors. The horizontal vector is computed as follows,and vertical vector is computed in a similar fashion. If both ({rightarrow over (v)}_(h1)=p_(left)−p,{right arrow over (v)}_(h2)=p_(right)−p)and ({right arrow over (v)}_(v1)=p_(top)−p,{right arrow over(v)}_(v2)=p_(bottom)−p) exist, the system checks their angle. If thesystem determines that the angle is too big (i.e., greater than apredetermined threshold value), the system takes the shorter one as{right arrow over (v)}_(horizontal); otherwise, the system takes theaverage as {right arrow over (v)}_(horizontal).

The HD map system determines 2650 normal of data point as a crossproduct of the horizontal and vertical vectors {right arrow over(v)}_(horizontal) and {right arrow over (v)}_(vertical). In anembodiment, the normal vector is adjusted to point to the LiDAR center.

FIG. 27 shows the process for performing pairwise alignment based onclassification of surfaces as hard/soft, according to an embodiment.Various steps illustrated in the flowchart may be performed by modulesother than those indicated herein. Certain steps may be performed in anorder different from those indicated herein.

The global alignment module 400 determines 2700 a plurality of 3Drepresentations of a portion of a geographical region. The 3Drepresentation may be a point cloud representation but could be anyother mechanism for modeling 3D objects and structures. The plurality of3D representations of a portion of a geographical region comprises afirst 3D representation R1 and a second 3D representation R2 of theportion of the geographical region. The portion of a geographical regionmay comprise a plurality of structures or objects, for example,buildings, trees, fences, walls, and so on.

The surface classification module 1140 determines 2710 a measure ofconfidence associated with data points indicative of a measure ofhardness of the surface corresponding to the data point. The surfaceclassification module 1140 considers structures and objects such asbuildings and walls are considered as relatively hard surfaces comparedto structures such as trees or objects such as living beings. Thesurface classification module 1140 determines a measure of hardness of asurface of a structure based on various criteria associated with theLIDAR signal returned by points on the surface of the structure. In anembodiment, each criterion is associated with a score. The measure ofhardness of the surface is determined as a weighted aggregate of thescores associated with the surface. A criterion for determining themeasure of hardness of a surface of a structure is the distribution ofthe normal vectors of points on the surface. Another criterion fordetermining the measure of hardness of a surface of a structure is thecolor of the light reflected by the surface. For example, the surfaceclassification module 1140 identifies certain colors as being indicativeof vegetation, for example, green. Accordingly, the surfaceclassification module 1140 associates surfaces having such colors with ahigh likelihood of being a soft surface such as a tree or any type ofvegetation. Another criterion used by the surface classification module1140 is the intensity of laser signal returned by the surface. Thesurface classification module 1140 associates higher intensity of signalreturned by a surface as an indication of hard surface. In anembodiment, various features associated with a point or a surface areextracted as a feature vector and provided to a machine learning basedmodel to determine a score indicating a level of hardness of a surface.In an embodiment, the machine learning based model is a classifier thatclassifies the surface as a hard surface or a soft surface. In anembodiment, the machine learning based model is trained using labeledsamples describing surfaces captured by sensors of vehicles. Furtherdetails of determining measures of confidence are further describedbelow in connection with FIGS. 28, 24, and 26.

The surface classification module 1140 determines 2730 a transformationT to map the first 3D representation to the second 3D representationbased on iterative closest point (ICP) techniques by weighing datapoints based on the measure of confidence for each data point. TheDetermine HD map system 100 determines 2740 a high definition map of thegeographical region by combining the first 3D representation R1 with thesecond 3D representation R2 using the transformation T. The HD mapsystem 100 stores the high definition map. The HD map system 100 usesthe high definition map for use in driving of autonomous vehicles.

The process described in FIG. 27 is heuristic based and may not measurean actual hardness of a structure. For example, a hard statue of a treewould be classified as a soft surface even though it is rigid and notmoveable. However, the technique provides accurate results in practicebecause the likelihood of encountering a statue or a rigid structurethat closely resembles a soft surface is low. Accordingly, statisticallythe results for most structures and objects encountered in practice areaccurate.

FIG. 28 shows the process for determining a measure of confidence forpoints along a surface for use in pairwise alignment, according to anembodiment. The surface classification module 1140 identifies a point ona surface of a structure. The surface classification module 1140determines a normal at the surface corresponding to the point. Thesurface classification module 1140 determine a measure of distributionof normals of neighboring points of the surface. The measure ofdistribution may be a statistical measure for example variance orstandard deviation. The surface classification module 1140 determines ameasure of confidence in the point at the surface as a value inverselyrelated to the measure of distribution of normals of neighboring pointsof the surface. Accordingly, the surface classification module 1140assigns scores indicating high confidence in points having low variancein the distribution of normals of neighboring points of the surface, forexample, walls, buildings, and so on. Similarly, the surfaceclassification module 1140 assigns scores indicating low confidence inpoints having high variance in the distribution of normals ofneighboring points of the surface, for example, trees.

FIG. 29 shows the process for determining a measure of confidence forpoints, according to an embodiment. The process in FIG. 29 is repeatedfor each point P with an estimated normal {right arrow over (n)}. Thesurface classification module 1140 initializes 2910 a counter N=0 and avariable V=0. The performs the steps 2920 and 2930 for each point Pi inpoint P's neighborhood in a rectangle window of the range image. Thesurface classification module 1140 determines 2920 the point-to-planedistance d_(i) for point Pi using equation d_(i)=(p_(i)−p)·{right arrowover (n)}. The surface classification module 1140 compares d_(i) againsta threshold value to determine whether the distance is below thethreshold. If the distance value is d_(i) determined to be below thethreshold value, the surface classification module 1140 adds a square ofd_(i) to the value of the variable V, i.e., V=V+d_(i) ² and increments Nby 1, e.g., by performing N++.

The surface classification module 1140 determines 2940 a value for thevariance as V/N. The surface classification module 1140 determines 2950a measure of confidence for point P as a function of variance. In anembodiment the measure of confidence for the normal vector is determinedas

${{confidence}\left( \overset{\rightarrow}{n} \right)} = {\exp \left( {- \frac{variance}{2\; \sigma^{2}}} \right)}$

where σ is a parameter. The surface classification module 1140determines confidence value for each normal, and combined the confidencevalues into the weights of each ICP cost so that higher weightscorrespond with high normal confidence as shown in the equation:

${\min\limits_{T}{\sum\limits_{i}{w_{i}{{n_{i} \cdot \left( {{T \cdot s_{i}} - d_{i}} \right)}}^{2}}}},{{{where}\mspace{14mu} w_{i}} = {{confidence}\left( {\overset{\rightarrow}{n}}_{i} \right)}}$

In some embodiments, in addition to weighting each correspondence vianormal confidence, the HD map system achieves further robustness againstnoises and outliers via weighting the correspondences based on thedistances between corresponding points. Given a correspondence[s_(i)→(d_(i), {right arrow over (n)}_(i))], the HD map system weightsthe correspondence via the corresponding point-to-plane error through aLorentz's function:

$w_{i} = {1/\left\lbrack {1 + {\frac{1}{2\; \sigma^{2}}\left( {{\overset{\rightarrow}{n}}_{i} \cdot \left( {s_{i} - d_{i}} \right)^{2}} \right)}} \right\rbrack}$

The Lorentz's function serves penalizes wrong correspondences therebymaking the result robust. In some embodiments, surface classificationmodule 1140 multiplies the Lorentz's weight with the normal confidenceas the final weight for each correspondence.

Soft surfaces are also referred to herein as softscape surfaces and hardsurfaces are referred to herein as hardscape surfaces. A surface has asurface type that can be hard or soft. A surface with hard surface typeis a hard surface, for example, a wall, and a surface with a softsurface type is a soft surface, for example, a tree.

The HD map system uses the confidence measure as a surface classifier,where “hardscape” surfaces have a high confidence and “softscape” has alow confidence. The HD map system uses this hardscape/softscapeclassification method to avoid matching hardscape points in one scan tosoftscape in the other, and uses the confidence weights to enforce thisconstraint.

The HD map system associates hardscape surfaces with higher confidencesince they are usually much more valuable for alignment than softscape.This is because when the LIDAR scans a hard surface like a wall, itreturns a clean, well-structured sets of points. So matching two scansof a wall is fairly easy. But when it scans softscape (like a bush), thepoints are quite noisy and have a complex random structure depending onhow deep into the bush the signal went, how the leaves were oriented,and so on. So when the HD map system tries to match two scans of a bush,the HD map system may downweight the normals and just use the points.

In general matching hardscape to hardscape provides a strong constraintand is weighted more by the HD map system. Softscape (vegetation) tosoftscape is useful but has a weaker constraint since one scan may hitone leaf and the 2nd scan may hit a different but nearby leaf.Accordingly, the HD map system weighs softscape to softscape surfacematching with lower weight compared to hardscape to hardscape surfacematching. Furthermore, the HD map system weighs hardscape to softscapematches the least as that is an indication of a matching error.

Accordingly, the HD map system determines the transformation to map afirst 3D representation to a second 3D representation by weighingcorresponding points higher if the points have surfaces with matchingsurface type compared to points that have different surface types. In anembodiment, the HD map system weighs corresponding points higher if thepoints are on surfaces with matching measure of hardness. Accordingly,two surfaces have a matching measure of hardness if the measure ofhardness of the two surfaces is within a threshold value each other.Furthermore, the HD map system weighs corresponding points higher if thepoints are on hard surfaces compared to points that are both on softsurfaces.

Detection of Misalignment Hotspots

After the HD map system performs global alignment, there are oftenpotential alignment issues that need further analysis, for example,quality assurance by humans or using automated tools. A human or anautomated tool could further provide input for improvement of thealignment data. However, the amount of data present in a HD map coveringa large area is huge. As a result, it is not practical to performdetailed follow-up analysis of the entire map, for example, by usinghuman operators to QA the entire map. Embodiments of the HD map systemimplement an automatic misalignment hotspot detection process toautomatically identify regions of alignment problems. Accordingly, thefollow-up analysis is performed of only the hotspots identified ratherthan of the entire HD map. This improves the efficiency of the processof finalizing the HD map and improves the quality of results. Forexample, the HD map needs to be finalized within a threshold amount oftime to be able to be provided for vehicles driving along a route. Ifthe process of verification and finalization of the HD map takes a verylong time, the changes to the HD map as a result of updates receivedfrom various autonomous vehicles cannot be propagated to other vehiclesdriving along a route. Embodiments make the process of verification andQA of the HD map efficient thereby allowing the HD map to be provided tosubsequent vehicles driving along various routes in time.

Embodiments automatically identify following situations that needfurther analysis and verification, for example, in the form of human QA.The HD map system detects via the misalignment hotspot detectionprocess: (1) Non-planar crossing of roads: e.g., over/under passes,bridges, etc. LIDAR samples have very little overlapping and hencedifficult to use ICP to align, but needs human to align them toguarantee global pose consistency. (2) Long-range loop closing: ICP canfail due to lack of overlapping point clouds, therefore the HD mapsystem does not automatically align point clouds that are far away(e.g., beyond 25 m) from each other. Since typical LiDARs have verylarge range (˜100 m), the point clouds may still have overlapping. Inthis case, the HD map system sends information to a user to manuallyclose loops by adding constraints between point clouds that are farapart but still have some portion of overlapping. (3) Alignment errorsthat lead to: misaligned ground, misaligned vertical planar walls, andso on in the OMap.

Overall Process for Detecting Misalignment Hotspots

After FIG. 30 shows a process for generating high-definition maps basedon automatic detection of misalignment hotspots, according to anembodiment. In an embodiment, the various steps illustrated in FIG. 30are performed by the online HD map system 110, for example, by theglobal alignment module 460. In other embodiments, some of the steps ofthe process may be performed by the vehicle computing system 120.

The HD map system 100 receives 3000 data from sensors of a plurality ofvehicles driving through a geographical region. For example, multiplevehicles may drive on the same road and send sensor data includingimages captured by cameras mounted on the care, LIDAR scan data, and soon. The HD map system 100 performs 3010 alignment of point clouds basedon the received data. For example, a point cloud for a portion of ageographical region determined based on sensor data received fromvehicle V1 may be slightly different from the point cloud for the sameportion of the geographical region determined based on sensor datareceived from vehicle V2. The global alignment module 460 performsalignment of the different point cloud representations to generate anaggregate point cloud representation for the geographical region.Accordingly, the global alignment module 460 generates a threedimensional representation of the region based on the received sensordata.

The misalignment hotspot detection module 1160 identifies 3030misalignment hotspots in the three dimensional representation of thegeographical region. The various techniques used by the hotspotdetection module 1160 to identify 3030 misalignment hotspots aredescribed in further details herein. In an embodiment, the HD map system100 builds 3040 a visual representation of the geographical region thathighlights the various misalignment hotspots, for example, the visualrepresentation may be a heat map chart. The HD map system 100 presents3050 the visual representation via a user interface of a display of aclient device. In an embodiment, the HD map system 100 receives 3060requests to modify the high definition map for the geographical regionvia a user interface. For example, a user may view the visualrepresentation and analyze the hotspots to identify problems with thealignment and then make corrections to the data representing thealignment results. In another embodiment, an automatic agent, forexample, an expert system may perform analysis of the misalignmenthotspots identified 3030 to recommend modifications to the HD map dataor to automatically make the modifications to the HD map data for thegeographical region.

In an embodiment, the misalignment hotspot detection module 1160 detectsmisalignment based on loop closing data. The misalignment hotspotdetection module 1160 detects non-planar crossing roads and long-rangeloop closing edges. For both case, given two arbitrary samples, themisalignment hotspot detection module 1160 computes the ratio betweentheir graph distance (i.e., shortest path distance determined bytraversing the pose graph) and their geodesic distance, i.e., thestraight line distance between two points. If the ratio is high, themisalignment hotspot detection module 1160 determines that there is ahigh likelihood that the two point clouds have overlapping portions, butdo not have loop closure pairwise transforms. Accordingly, themisalignment hotspot detection module 1160 indicates these portions asmisalignment hotspots.

In another embodiment, the misalignment hotspot detection module 1160detects misaligned ground in the three dimensional representation of thegeographical region. Perfectly aligned ground should be a single layerof nodes, but practice the aligned ground nodes determined by the globalalignment module 400 has a thickness that has a non-zero value. Themisalignment hotspot detection module 1160 determines that thick layersof ground nodes indicate bad alignment, while thin layers indicate goodalignment. Accordingly, the misalignment hotspot detection module 1160determines a likelihood of misalignment as a value directly related tothe thickness of the layer representing ground in a portion of thegeographical region.

FIG. 31 shows a process for detection of misalignment for surfacesrepresented in a point cloud, according to an embodiment. The processshown in FIG. 31 is an embodiment of the step 3030 shown in FIG. 30. Themisalignment hotspot detection module 1160 identifies a surface 3100 inthe three dimensional representation of a geographical region, forexample, a point cloud representation. For example, the surface mayrepresent ground or a wall. The misalignment hotspot detection module1160 determines 3110 a normal to the identified surface. Themisalignment hotspot detection module 1160 identifies 3120 data pointswithin the point cloud representation along the normal direction as wellas the direction opposite to the normal. The misalignment hotspotdetection module 1160 selects 3130 a cluster of points that are likelyto represent the identified surface, for example, the misalignmenthotspot detection module 1160 may cluster the identified data points andselect a cluster representing the nearest neighbor points that are closeto the identified surface.

In an embodiment, the misalignment hotspot detection module 1160 startwith an identified data point that is closest to the identified surface.The misalignment hotspot detection module 1160 builds a set of datapoints by adding data points to the set if they are within a thresholddistance of at least one other data point within the set. Themisalignment hotspot detection module 1160 when the nearest data pointto the selected set of data points is more than the threshold distance.

The misalignment hotspot detection module 1160 determines 3140 themaximum distance between the selected data points along the normal thatare determined to represent the surface. The misalignment hotspotdetection module 1160 determines QX50 a measure of misalignment at aparticular data point along the surface as a value that is directlyrelated to or directly proportionate to the determines 3140 maximumdistance between the selected data points along the normal correspondingto that particular data point on the surface.

FIG. 32 illustrates detection of misalignment for ground represented ina point cloud, according to an embodiment. In the OMap building process,the HD map system ingests interpolated ground points into a verticalcolumn's temporary memory storage. When the HD map system ingests allground points, the average height value (along the Z axis) is calculatedbased on the all of the z-coordinate values in that vertical column. Themisalignment hotspot detection module 1160 determines the average zvalue, and determines the thickness of the ground representation as thedifference between the maximum value of the z coordinate (max_z) and theminimum value of the z-coordinate (min_z) within the vertical column.The misalignment hotspot detection module 1160 uses the measure ofthickness of the ground along the vertical column as an indicator ofmisalignment. Accordingly, the misalignment hotspot detection module1160 determines a score representing the likelihood of misalignment as avalue directly proportionate (or directly related) to the differencebetween the max_z and min_z values. In one embodiment, the misalignmenthotspot detection module 1160 uses one byte (0-255) to represent thepossibility of misalignment, with 0 representing perfectly alignedground and 255 representing the diff(max_z, min_z)>=50 cm. For example,a list of node z values could be [12, 13, 14, 16], the average of node zis 14 (13.75 almost equal to 14) and the diff(max_z, min_z)=4. Each nodeis about 5 centimeters, so converting to metric value, the diff is 20centimeters. The misalignment hotspot detection module 1160 determinesthe one byte value as (255*20/50.0)=100 (approximately).

FIG. 33 shows a process for detection of misalignment for ground surfacerepresented in a point cloud, according to an embodiment. The processshown in FIG. 31 is an embodiment of the step 3030 shown in FIG. 30. Themisalignment hotspot detection module 1160 identifies 3300 a noderepresenting a portion of ground surface in the three dimensionalrepresentation of a region. The misalignment hotspot detection module1160 maps 3310 a plurality of data points corresponding to the node to avertical column within the three dimensional representation. The normalto the ground surface is assumed to be in the vertical direction, i.e.,the Z axis. The vertical column comprises data points having the same xand y coordinate values by varying z coordinate values. The misalignmenthotspot detection module 1160 determines 3320 the maximum z coordinateand the minimum z coordinate values in the plurality of data points. Themisalignment hotspot detection module 1160 determines 3330 a measure ofmisalignment for the portion of ground based on a value proportionate tothe difference between the maximum z coordinate value and the minimum zcoordinate value.

FIG. 34 shows a process for detection of misalignment for verticalsurfaces represented in a point cloud, according to an embodiment. Theprocess shown in FIG. 34 is an embodiment of the step 3030 shown in FIG.30. The misalignment hotspot detection module 1160 identifies 3400non-ground nodes in the three dimensional representation of ageographical region. The misalignment hotspot detection module 1160identifies 3410 data points that represent a vertical planar surface. Inan embodiment, the misalignment hotspot detection module 1160 uses aplane segmentation technique for identifying 3410 data points thatrepresent a vertical planar surface.

The misalignment hotspot detection module 1160 repeats the followingsteps 3420, 3430, 3440, and 3450 for each node of the vertical surfaceor at least a subset of nodes for the vertical surface. The misalignmenthotspot detection module 1160 identifies 3420 a normal to the verticalsurface. The normal represents a vector in a horizontal plane. Themisalignment hotspot detection module 1160 identified data points alongthe normal direction and direction opposite to the normal. Themisalignment hotspot detection module 1160 selects a cluster of datapoints that have the same z coordinate value that are close to thevertical surface. The misalignment hotspot detection module 1160determines 3450 a measure of misalignment at the node as a valueproportionate to the distance between the farthest points within thecluster.

The misalignment hotspot detection module 1160 further determines anaverage measure of misalignment for each vertical column of nodes. Themisalignment hotspot detection module 1160 converts the result to a 2Dheat map image, so each image pixel represents the averaged probabilityof misalignment of one vertical column.

FIG. 35 shows an example illustrating detection of misalignment for avertical structure such as a wall represented in a point cloud,according to an embodiment. Similar to ground alignment, themisalignment hotspot detection module 1160 treats walls as thin verticalplanar layers. However, misalignment can cause duplicate and thick walllayers. The misalignment hotspot detection module 1160 identifiesmisaligned walls or portions within any vertical surface that aremisaligned. The HD Map system treats the built OMap as a point cloud byconsidering the center of each node as a point. The misalignment hotspotdetection module 1160 obtains non-ground nodes since they representvertical surfaces. The misalignment hotspot detection module 1160 uses aplane segmentation algorithm to get all planar surfaces, for example, asub-window based region growing (SBRG) algorithm. Since the point cloudis converted from the OMap, all temporary obstacle points (e.g. cars)are not included. The misalignment hotspot detection module 1160 marksthe segmented points as walls or vertical surfaces.

According to an embodiment, the misalignment hotspot detection module1160 performs the following steps. In the following description, pointsin the three dimensional representation of the geographical region arereferred to as nodes. The misalignment hotspot detection module 1160marks all wall nodes as unknown.

The misalignment hotspot detection module 1160 performs the followingsteps for each unknown wall node. The misalignment hotspot detectionmodule 1160 gets the normal of the node. Along the normal direction andthe reverse direction, the misalignment hotspot detection module 1160finds the minimum x coordinate (min_x), maximum x coordinate (max_x),minimum y coordinate (min_y) and maximum y coordinate (max_y) in thesame z level. The normal of the found nodes should match the normal ofthe current wall node being processed. The purpose is not to select theother side of the wall nodes. In an embodiment, the misalignment hotspotdetection module 1160, builds a KD-tree of all the wall nodes. Themisalignment hotspot detection module 1160 searches all wall nodeswithin a threshold distance (for example, 1 meter) for the current wallnode being processed. The misalignment hotspot detection module 1160identifies nearest neighbor points for the wall node. The hotspotdetection module 1160 excludes (or skips) some of the points based oncertain criteria. The misalignment hotspot detection module 1160excludes the point if node z coordinate is different. The misalignmenthotspot detection module 1160 excludes the point if the angle betweenthe current node's normal and the neighbor node's normal is larger thana threshold. The misalignment hotspot detection module 1160 excludes thepoint if the vector direction from neighbor node to current node is notparallel to the current node's normal. The misalignment hotspotdetection module 1160 updates the min_x, max_x, min_y and max_y valuesif needed. The misalignment hotspot detection module 1160 marks thenearest neighbor node as DONE.

The misalignment hotspot detection module 1160 uses the min_x, max_x,min_y and max_y to calculate the distance to indicate the misalignmentthickness. For example, min_x=2, max_x=5, min_y=10, max_y=20, results inthe distance of 10.4 which roughly equals to 52 (10.4*5 cm) centimeters.Similar to the ground misalignment probability value, in an embodiment,the HD map system uses one byte to represent the value. The HD mapsystem marks all these nodes as DONE from unknown so they don't need tobe calculated again.

After all wall nodes are done, the misalignment hotspot detection module1160 determines for each vertical column, the averaged misalignmentprobability of all wall nodes in this vertical column and assigns thatvalue to the vertical column. To determine the average of misalignmentprobability, misalignment hotspot detection module 1160 adds all thewall node misalignment probability values in the same vertical column.The misalignment hotspot detection module 1160 divides the resultingvalue by the total number of wall nodes in that vertical column to getthe averaged misalignment probability.

In an embodiment, the misalignment hotspot detection module 1160 exportsthe vertical column probabilities or ground misalignment probabilitiesas a 2D image as a heat map. For example, the misalignment hotspotdetection module 1160 may color the high probability of misalignment asred and the well aligned x, y positions as green. The HD map systemprovides the generated heat map to a user to allow the user to visuallyinspect the OMap and focus on areas identified as having highprobability of misalignment.

The generated heat map can also be used to generate hotspot locations byusing the method described below. This process applies to both groundmisalignment probability and wall misalignment probability heat maps.When a 2D heat map is generated, the HD map system determines hotspotlocations from it by using an n×n moving window (e.g. n=5). If there aremore than 50% (when n=5, the actual number is 13, 25*0.5=12.5) pixelvalues that are larger than 100 (means the misalignment is around 20 cm.255*20 cm/50 cm=102), the latitude/longitude of the hotspot is exportedto the output. Thus the final result is a list of hotspotlatitude/longitude locations which is provided to a review tool to allowoperators to review data for those locations and check the alignmentmanually.

According to another embodiment, hotspots are identified by clusteringpixel values greater than 100 using a clustering algorithm which mergesneighboring clusters while keeping the constraint of cluster diameter tobe roughly 10 m (i.e., a value that is similar to the scale of analignment sampling). The HD map system generates a hotspot for eachcluster and exports it to a review tool.

Computing Machine Architecture

FIG. 36 is a block diagram illustrating components of an example machineable to read instructions from a machine-readable medium and executethem in a processor (or controller). Specifically, FIG. 36 shows adiagrammatic representation of a machine in the example form of acomputer system 3600 within which instructions 3624 (e.g., software) forcausing the machine to perform any one or more of the methodologiesdiscussed herein may be executed. In alternative embodiments, themachine operates as a standalone device or may be connected (e.g.,networked) to other machines. In a networked deployment, the machine mayoperate in the capacity of a server machine or a client machine in aserver-client network environment, or as a peer machine in apeer-to-peer (or distributed) network environment.

The machine may be a server computer, a client computer, a personalcomputer (PC), a tablet PC, a set-top box (STB), a personal digitalassistant (PDA), a cellular telephone, a smartphone, a web appliance, anetwork router, switch or bridge, or any machine capable of executinginstructions 3624 (sequential or otherwise) that specify actions to betaken by that machine. Further, while only a single machine isillustrated, the term “machine” shall also be taken to include anycollection of machines that individually or jointly execute instructions3624 to perform any one or more of the methodologies discussed herein.

The example computer system 3600 includes a processor 3602 (e.g., acentral processing unit (CPU), a graphics processing unit (GPU), adigital signal processor (DSP), one or more application specificintegrated circuits (ASICs), one or more radio-frequency integratedcircuits (RFICs), or any combination of these), a main memory 3604, anda static memory 3606, which are configured to communicate with eachother via a bus 3608. The computer system 3600 may further includegraphics display unit 3610 (e.g., a plasma display panel (PDP), a liquidcrystal display (LCD), a projector, or a cathode ray tube (CRT)). Thecomputer system 3600 may also include alphanumeric input device 3612(e.g., a keyboard), a cursor control device 3614 (e.g., a mouse, atrackball, a joystick, a motion sensor, or other pointing instrument), astorage unit 3616, a signal generation device 3618 (e.g., a speaker),and a network interface device 3620, which also are configured tocommunicate via the bus 3608.

The storage unit 3616 includes a machine-readable medium 3622 on whichis stored instructions 3624 (e.g., software) embodying any one or moreof the methodologies or functions described herein. The instructions3624 (e.g., software) may also reside, completely or at least partially,within the main memory 3604 or within the processor 3602 (e.g., within aprocessor's cache memory) during execution thereof by the computersystem 3600, the main memory 3604 and the processor 3602 alsoconstituting machine-readable media. The instructions 3624 (e.g.,software) may be transmitted or received over a network 3626 via thenetwork interface device 3620.

While machine-readable medium 3622 is shown in an example embodiment tobe a single medium, the term “machine-readable medium” should be takento include a single medium or multiple media (e.g., a centralized ordistributed database, or associated caches and servers) able to storeinstructions (e.g., instructions 3624). The term “machine-readablemedium” shall also be taken to include any medium that is capable ofstoring instructions (e.g., instructions 3624) for execution by themachine and that cause the machine to perform any one or more of themethodologies disclosed herein. The term “machine-readable medium”includes, but not be limited to, data repositories in the form ofsolid-state memories, optical media, and magnetic media.

Additional Configuration Considerations

The foregoing description of the embodiments of the invention has beenpresented for the purpose of illustration; it is not intended to beexhaustive or to limit the invention to the precise forms disclosed.Persons skilled in the relevant art can appreciate that manymodifications and variations are possible in light of the abovedisclosure.

For example, although the techniques described herein are applied toautonomous vehicles, the techniques can also be applied to otherapplications, for example, for displaying HD maps for vehicles withdrivers, for displaying HD maps on displays of client devices such asmobile phones, laptops, tablets, or any computing device with a displayscreen. Techniques displayed herein can also be applied for displayingmaps for purposes of computer simulation, for example, in computergames, and so on.

Some portions of this description describe the embodiments of theinvention in terms of algorithms and symbolic representations ofoperations on information. These algorithmic descriptions andrepresentations are commonly used by those skilled in the dataprocessing arts to convey the substance of their work effectively toothers skilled in the art. These operations, while describedfunctionally, computationally, or logically, are understood to beimplemented by computer programs or equivalent electrical circuits,microcode, or the like. Furthermore, it has also proven convenient attimes, to refer to these arrangements of operations as modules, withoutloss of generality. The described operations and their associatedmodules may be embodied in software, firmware, hardware, or anycombinations thereof.

Any of the steps, operations, or processes described herein may beperformed or implemented with one or more hardware or software modules,alone or in combination with other devices. In one embodiment, asoftware module is implemented with a computer program productcomprising a computer-readable medium containing computer program code,which can be executed by a computer processor for performing any or allof the steps, operations, or processes described.

Embodiments of the invention may also relate to an apparatus forperforming the operations herein. This apparatus may be speciallyconstructed for the required purposes, and/or it may comprise ageneral-purpose computing device selectively activated or reconfiguredby a computer program stored in the computer. Such a computer programmay be stored in a tangible computer readable storage medium or any typeof media suitable for storing electronic instructions, and coupled to acomputer system bus. Furthermore, any computing systems referred to inthe specification may include a single processor or may be architecturesemploying multiple processor designs for increased computing capability.

Embodiments of the invention may also relate to a computer data signalembodied in a carrier wave, where the computer data signal includes anyembodiment of a computer program product or other data combinationdescribed herein. The computer data signal is a product that ispresented in a tangible medium or carrier wave and modulated orotherwise encoded in the carrier wave, which is tangible, andtransmitted according to any suitable transmission method.

Finally, the language used in the specification has been principallyselected for readability and instructional purposes, and it may not havebeen selected to delineate or circumscribe the inventive subject matter.It is therefore intended that the scope of the invention be limited notby this detailed description, but rather by any claims that issue on anapplication based hereon.

What is claimed is:
 1. A method for distributed processing of posegraphs for generating high-definition maps, the method comprising:receiving sensor data captured by a plurality of vehicles drivingthrough a path in a geographical region; generating a pose graph,wherein each node of the graph represents a pose of a vehicle, the posecomprising a location and orientation of the vehicle, and wherein eachedge between a pair of nodes represents a transformation between nodesof the pair of nodes; dividing the pose graph into a plurality ofsubgraphs, each subgraph including a set of core nodes and a set ofbuffer nodes; iteratively performing the steps comprising: for each subpose graph, keeping values of boundary nodes fixed and optimizing subpose graph, and updating all node poses using core node poses of all subpose graphs; generating a high-definition map based on the point cloudrepresentation; and sending the high-definition map to one or moreautonomous vehicles for navigating the autonomous vehicles in thegeographical region.
 2. The method of claim 1, wherein the sensor datacomprises data collected by LIDAR, data collected by global positioningsystem (GPS), and data collected by inertial measurement unit (IMU). 3.The method of claim 1, wherein repeatedly performing the steps furthercomprises, determining whether there are changes in boundary nodes as aresult of updating all node poses, and stopping the iterationsresponsive to determining that the changes to boundary nodes are below athreshold value.
 4. The method of claim 1, wherein the subgraphs aredistributed among a plurality of processors for distributed execution.5. The method of claim 1, wherein dividing the pose graph into aplurality of subgraphs comprises: determining bounding boxes based onlatitude and longitude values; and assigning all poses within a boundingbox to a subgraph.
 6. The method of claim 1, wherein dividing the posegraph into a plurality of subgraphs comprises dividing the pose graphalong a road to obtain subgraphs.
 7. The method of claim 1, whereindividing the pose graph into a plurality of subgraphs comprises dividingthe pose graph along a junction in roads to obtain subgraphs.
 8. Themethod of claim 1, wherein dividing the pose graph into a plurality ofsubgraphs comprises dividing the pose graph with the objective ofminimizing the number of boundary nodes.
 9. The method of claim 1,wherein dividing the pose graph into a plurality of subgraphs comprisesidentifying portions of a geographical region that have large number ofsamples returned by vehicles and dividing the pose graph into subgraphssuch that a boundary of a subgraph pass through an identified portion ofthe geographical region.
 10. A non-transitory computer readable storagemedium storing instructions for: receiving sensor data captured by aplurality of vehicles driving through a path in a geographical region;generating a pose graph, wherein each node of the graph represents apose of a vehicle, the pose comprising a location and orientation of thevehicle, and wherein each edge between a pair of nodes represents atransformation between nodes of the pair of nodes; dividing the posegraph into a plurality of subgraphs, each subgraph including a set ofcore nodes and a set of buffer nodes; iteratively performing the stepscomprising: for each sub pose graph, keeping values of boundary nodesfixed and optimizing sub pose graph, and updating all node poses usingcore node poses of all sub pose graphs; generating a high-definition mapbased on the point cloud representation; and sending the high-definitionmap to one or more autonomous vehicles for navigating the autonomousvehicles in the geographical region.
 11. The non-transitory computerreadable storage medium of claim 10, wherein the sensor data comprisesdata collected by LIDAR, data collected by global positioning system(GPS), and data collected by inertial measurement unit (IMU).
 12. Thenon-transitory computer readable storage medium of claim 10, whereinrepeatedly performing the steps further comprises, determining whetherthere are changes in boundary nodes as a result of updating all nodeposes, and stopping the iterations responsive to determining that thechanges to boundary nodes are below a threshold value.
 13. Thenon-transitory computer readable storage medium of claim 10, wherein thesubgraphs are distributed among a plurality of processors fordistributed execution.
 14. The non-transitory computer readable storagemedium of claim 10, wherein instructions for dividing the pose graphinto a plurality of subgraphs comprise instructions for: determiningbounding boxes based on latitude and longitude values; and assigning allposes within a bounding box to a subgraph.
 15. The non-transitorycomputer readable storage medium of claim 10, wherein dividing the posegraph into a plurality of subgraphs comprises dividing the pose graphalong a road to obtain subgraphs.
 16. The non-transitory computerreadable storage medium of claim 10, wherein dividing the pose graphinto a plurality of subgraphs comprises dividing the pose graph along ajunction in roads to obtain subgraphs.
 17. The non-transitory computerreadable storage medium of claim 10, wherein dividing the pose graphinto a plurality of subgraphs comprises dividing the pose graph with theobjective of minimizing the number of boundary nodes.
 18. Thenon-transitory computer readable storage medium of claim 10, whereindividing the pose graph into a plurality of subgraphs comprisesidentifying portions of a geographical region that have large number ofsamples returned by vehicles and dividing the pose graph into subgraphssuch that a boundary of a subgraph pass through an identified portion ofthe geographical region.
 19. A computer system comprising: an electronicprocessor; and a non-transitory computer readable storage medium storinginstructions executable by the electronic processor, the instructionsfor: receiving sensor data captured by a plurality of vehicles drivingthrough a path in a geographical region; generating a pose graph,wherein each node of the graph represents a pose of a vehicle, the posecomprising a location and orientation of the vehicle, and wherein eachedge between a pair of nodes represents a transformation between nodesof the pair of nodes; dividing the pose graph into a plurality ofsubgraphs, each subgraph including a set of core nodes and a set ofbuffer nodes; iteratively performing the steps comprising: for each subpose graph, keeping values of boundary nodes fixed and optimizing subpose graph, and updating all node poses using core node poses of all subpose graphs; generating a high-definition map based on the point cloudrepresentation; and sending the high-definition map to one or moreautonomous vehicles for navigating the autonomous vehicles in thegeographical region.
 20. The computer system of claim 19, whereininstructions for dividing the pose graph into a plurality of subgraphscomprise instructions for: determining bounding boxes based on latitudeand longitude values; and assigning all poses within a bounding box to asubgraph.